| --- |
| tags: |
| - seismic |
| - random-noise |
| - denoising |
| - unet |
| - resunet |
| - dncnn |
| - attention-unet |
| - ddpm |
| - scrn |
| - pytorch |
| library_name: pytorch |
| --- |
| |
| # Random Noise Suppression AVO Benchmark |
|
|
| Deep-learning-based random-noise attenuation on pre-stack seismic shot gathers, using the clean seismic.segy AVO dataset and synthetic Gaussian / Poisson noise injection. |
|
|
| ## Task |
|
|
| Given a clean shot gather, the benchmark first injects synthetic random noise at a specified SNR, then trains a model to directly reconstruct the clean signal: |
|
|
| ```python |
| denoised = model(noisy_input) |
| ``` |
|
|
| This is a **paired regression** task with clean-target supervision. Most models directly predict the clean gather. The DDPM variant predicts diffusion noise during training and reconstructs the clean gather through reverse sampling at evaluation / inference time. |
|
|
| ## Dataset |
|
|
| - **Source**: Clean AVO seismic.segy data |
| - **Current training volume**: `seismic.segy` |
| - **Geometry**: 201 traces per shot, time sampling interval `dt = 2 ms` |
| - **Split**: Shot-level sequential `7:1:1` |
| - 801 training shots |
| - 100 validation shots |
| - 100 held-out test shots |
|
|
| ### Synthetic Noise Settings |
|
|
| - **Noise kinds**: `gaussian`, `poisson` |
| - **Default sweep in training / inference scripts**: `SNR = -5, 0, 5 dB` |
| - **Noise injection**: per-shot variance-controlled synthetic corruption |
| - **Reproducibility**: noise generation is seeded from `experiment.seed` |
|
|
| ## Model Architectures |
|
|
| - **UNet** (`unet`): classic encoder-decoder with skip connections. Base channels: 32, depth: 4. |
| - **ResUNet** (`res_unet`): U-Net with residual blocks. Base channels: 32, depth: 4. |
| - **DnCNN** (`dncnn`): residual denoising CNN with 17 layers and 64 feature channels. |
| - **Attention UNet** (`atten_unet`): U-Net with attention gates. Base channels: 32, depth: 4. |
| - **DDPM** (`ddpm`): standard conditional denoising diffusion probabilistic model with reverse sampling from Gaussian noise to the clean shot gather. |
| - **SCRN** (`SCRN`): Swin Transformer convolutional residual network adapted to the same random-noise benchmark pipeline. |
|
|
| ## Preprocessing |
|
|
| - **Amplitude correction**: spherical divergence correction is skipped by default |
| - **Normalization**: `max_abs`, per-shot |
| - **Patching**: overlapping 2D patches of size `128 x 256` (`trace x time`) |
| - **Patch overlap**: `50%` |
|
|
| Training uses patched shot gathers. Inference reloads the raw volume, applies `inference.shot_split`, injects synthetic noise, runs patch-based reconstruction, and inverse-normalizes outputs for visualization. |
|
|
| ## Repository Structure |
|
|
| ```text |
| scripts/random_noise_suppression_avo/ |
| |- train_denoise_unet.sh |
| |- train_denoise_res_unet.sh |
| |- train_denoise_dncnn.sh |
| |- train_denoise_atten_unet.sh |
| |- train_denoise_ddpm.sh |
| |- train_denoise_SCRN.sh |
| |- inference_denoise_unet.sh |
| |- inference_denoise_res_unet.sh |
| |- inference_denoise_dncnn.sh |
| |- inference_denoise_atten_unet.sh |
| |- inference_denoise_ddpm.sh |
| |- inference_denoise_SCRN.sh |
| `- run_all_random_noise_models.sh |
| |
| configs/random_noise_suppression_avo/ |
| |- denoise_unet.yaml |
| |- denoise_res_unet.yaml |
| |- denoise_dncnn.yaml |
| |- denoise_atten_unet.yaml |
| |- denoise_ddpm.yaml |
| `- denoise_SCRN.yaml |
| ``` |
|
|
| Each experiment directory is named by model, noise kind, SNR, and seed, for example: |
|
|
| ```text |
| random_noise_avo_unet_base_gaussian_snr5_seed42/ |
| random_noise_avo_dncnn_base_poisson_snr0_seed43/ |
| random_noise_avo_ddpm_base_gaussian_snrneg5_seed44/ |
| ``` |
|
|
| ## Training Details |
|
|
| Shared benchmark defaults: |
|
|
| | Hyperparameter | Value | |
| |---|---| |
| | Loss | MSE | |
| | DDPM note | trains on diffusion-noise prediction and validates clean reconstruction after reverse sampling | |
| | Optimizer | AdamW (`lr=1e-4`, `weight_decay=1e-5`) | |
| | Scheduler | Cosine annealing (`min_lr=1e-6`) | |
| | Epochs | 200 | |
| | Gradient clipping | 1.0 | |
| | Seeds | 42, 43, 44 by default in shell sweeps | |
| | Batch size | 192 in current YAML defaults | |
|
|
| ## Usage |
|
|
| ### Train One Model Family |
|
|
| ```bash |
| bash scripts/random_noise_suppression_avo/train_denoise_unet.sh |
| ``` |
|
|
| or |
|
|
| ```bash |
| bash scripts/random_noise_suppression_avo/train_denoise_ddpm.sh |
| ``` |
|
|
| Each training shell script sweeps: |
|
|
| - noise kind |
| - SNR |
| - seed |
|
|
| by rewriting a temporary YAML config before calling `torchrun`. |
|
|
| ### Run Inference |
|
|
| ```bash |
| bash scripts/random_noise_suppression_avo/inference_denoise_unet.sh |
| ``` |
|
|
| Inference outputs: |
|
|
| - per-shot metrics CSV |
| - summary JSON |
| - visualizations |
| - optional `.npy` files |
| - multi-seed mean/std aggregation JSON |
|
|
| ### Run All Model Families |
|
|
| ```bash |
| bash scripts/random_noise_suppression_avo/run_all_random_noise_models.sh |
| ``` |
|
|
| Current total-run script executes: |
|
|
| 1. `unet` |
| 2. `dncnn` |
| 3. `res_unet` |
| 4. `atten_unet` |
| 5. `ddpm` |
|
|
| Each model is trained first, then its inference sweep is launched immediately after training finishes. |
|
|
| ## Inference Outputs |
|
|
| For each experiment, the inference directory typically contains: |
|
|
| ```text |
| inference/ |
| |- inference.log |
| |- metrics_per_shot.csv |
| |- metrics_summary.json |
| |- visualizations/ |
| `- npy/ # only when save_npy=true |
| ``` |
|
|
| ### Metrics |
|
|
| The benchmark reports: |
|
|
| - `snr` |
| - `psnr` |
| - `ssim` |
| - `mae` |
| - `mse` |
| - `rmse` |
|
|
| for three groups: |
|
|
| - **noisy**: noisy input vs clean target |
| - **denoised**: model prediction vs clean target |
| - **delta**: `denoised - noisy` |
|
|
| Metrics are computed in the **normalized domain**. Saved visualization outputs are inverse-normalized back to the original amplitude domain. |
|
|
| ## Notes |
|
|
| - This benchmark injects **synthetic random noise once per experiment** before patch extraction; it is not an epoch-wise dynamic noise augmentation setup. |
| - Shot-level inference uses the held-out test shot defined by `inference.shot_split`. |
| - Batch size and patch size may need adjustment for memory-heavy models such as DnCNN and DDPM. |
|
|
| ## References |
|
|
| - Ronneberger et al., U-Net: Convolutional Networks for Biomedical Image Segmentation, MICCAI 2015 |
| - He et al., Deep Residual Learning for Image Recognition, CVPR 2016 |
| - Zhang et al., Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising, IEEE TIP 2017 |
| - Ho et al., Denoising Diffusion Probabilistic Models, NeurIPS 2020 |
| - Song et al., Denoising Diffusion Implicit Models, ICLR 2021 |
| - Oktay et al., Attention U-Net: Learning Where to Look for the Pancreas, MIDL 2018 |
| - Gao et al., Swin Transformer for simultaneous denoising and interpolation of seismic data, Computers and Geosciences 2024 |
| - SEG C3 Velocity Model: https://wiki.seg.org/wiki/C3 |
|
|
| <!-- AUTO_RESULTS_BEGIN --> |
| ## Results |
|
|
| Mean +- std over available seeds, computed from `*_seed_stats/metrics_summary_mean_std.json`. |
| Metrics are reported in the normalized domain. `Raw (noisy)` is the synthetic noisy input before denoising. |
|
|
| ### Gaussian Noise |
|
|
| #### SNR -5 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | -5.0000+-0.0015 | 28.4184+-0.0015 | 0.6663+-0.0002 | 0.030315+-0.000008 | 0.001448+-0.000001 | 0.037994+-0.000007 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 1.841567+-0.000122 | -5.2710+-0.0006 | 0.283209+-0.000000 | 6.3333-23.1333 | 1.150310+-0.000255 | -1.2055+-0.0019 | 0.425271+-0.000000 | 23.1333-48.3333 | 1.147630+-0.000330 | -1.1892+-0.0025 | 0.001317+-0.000000 | 73.5333-90.3333 | 18.962028+-0.014011 | -25.2465+-0.0056 | |
| | SCRN | 0.43 | 12.7292+-0.0049 | 46.1476+-0.0049 | 0.9911+-0.0000 | 0.002425+-0.000002 | 0.000024+-0.000000 | 0.004932+-0.000003 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.245962+-0.000458 | 12.2091+-0.0165 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.225842+-0.000225 | 12.9308+-0.0086 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.222855+-0.000167 | 13.0470+-0.0072 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.944941+-0.000954 | 0.5184+-0.0101 | |
| | Attention UNet | 7.85 | 9.2655+-0.0043 | 42.6839+-0.0043 | 0.9841+-0.0000 | 0.003342+-0.000001 | 0.000054+-0.000000 | 0.007349+-0.000003 | 1.420080+-0.001250 | -3.0417+-0.0072 | 0.338450+-0.000173 | 9.4140+-0.0045 | 10.383467+-0.018701 | -20.2944+-0.0172 | 3.086211+-0.005139 | -9.7630+-0.0130 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.379595+-0.000461 | 8.4401+-0.0106 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.316613+-0.000383 | 9.9980+-0.0105 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.300578+-0.000210 | 10.4476+-0.0062 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.982228+-0.000575 | -5.7680+-0.0028 | |
| | DDPM | 33.30 | 7.1648+-0.1578 | 40.5832+-0.1578 | 0.9638+-0.0010 | 0.006926+-0.000121 | 0.000089+-0.000004 | 0.009404+-0.000181 | 6.014312+-0.110613 | -15.4517+-0.1591 | 0.308027+-0.003648 | 10.2626+-0.0994 | 47.100793+-1.606397 | -33.1509+-0.2805 | 21.533452+-0.800196 | -26.4012+-0.2870 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.460230+-0.009898 | 6.8349+-0.1815 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.327191+-0.004703 | 9.7471+-0.1208 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.334689+-0.005003 | 9.5485+-0.1310 | 0.001317+-0.000000 | 73.5333-90.3333 | 3.874504+-0.119669 | -11.4459+-0.2489 | |
| | DnCNN | 0.56 | 8.9707+-0.0035 | 42.3891+-0.0035 | 0.9810+-0.0000 | 0.004693+-0.000001 | 0.000058+-0.000000 | 0.007602+-0.000003 | 3.098011+-0.001746 | -9.8080+-0.0049 | 0.324189+-0.000137 | 9.7859+-0.0037 | 20.750542+-0.000822 | -26.3332+-0.0001 | 9.744805+-0.001931 | -19.7626+-0.0019 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.367070+-0.000116 | 8.7256+-0.0027 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.336299+-0.000336 | 9.4715+-0.0087 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.297151+-0.000085 | 10.5476+-0.0027 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.415984+-0.004356 | -7.4367+-0.0128 | |
| | ResUNet | 8.11 | 8.0137+-0.0010 | 41.4321+-0.0010 | 0.9801+-0.0000 | 0.005013+-0.000001 | 0.000072+-0.000000 | 0.008487+-0.000001 | 3.284017+-0.000458 | -10.3149+-0.0014 | 0.364963+-0.000046 | 8.7602+-0.0010 | 22.633412+-0.010049 | -27.0879+-0.0039 | 10.442392+-0.004309 | -20.3616+-0.0033 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.442797+-0.000388 | 7.1028+-0.0073 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.335051+-0.000210 | 9.5125+-0.0057 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.324708+-0.000281 | 9.7780+-0.0074 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.940459+-0.002013 | -9.1227+-0.0075 | |
| | UNet | 7.76 | 8.9440+-0.0034 | 42.3624+-0.0034 | 0.9807+-0.0000 | 0.004273+-0.000002 | 0.000058+-0.000000 | 0.007623+-0.000003 | 2.416080+-0.000817 | -7.6466+-0.0029 | 0.338792+-0.000149 | 9.4048+-0.0037 | 15.867472+-0.007823 | -23.9986+-0.0043 | 7.376587+-0.006652 | -17.3406+-0.0071 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.371155+-0.000550 | 8.6215+-0.0129 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.350323+-0.000279 | 9.1190+-0.0065 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.308464+-0.000132 | 10.2247+-0.0039 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.857709+-0.001298 | -5.2139+-0.0053 | |
|
|
| #### SNR 0 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | 0.0000+-0.0015 | 33.4184+-0.0015 | 0.8644+-0.0001 | 0.017047+-0.000004 | 0.000458+-0.000000 | 0.021365+-0.000004 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 1.035589+-0.000068 | -0.2710+-0.0006 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.646867+-0.000144 | 3.7945+-0.0019 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.645360+-0.000186 | 3.8108+-0.0025 | 0.001317+-0.000000 | 73.5333-90.3333 | 10.663132+-0.007879 | -20.2465+-0.0056 | |
| | SCRN | 0.43 | 15.0992+-0.0025 | 48.5176+-0.0025 | 0.9946+-0.0000 | 0.001964+-0.000001 | 0.000014+-0.000000 | 0.003754+-0.000001 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.192994+-0.000116 | 14.3184+-0.0054 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.166057+-0.000083 | 15.6015+-0.0043 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.168152+-0.000058 | 15.4936+-0.0034 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.881094+-0.000434 | 1.1330+-0.0035 | |
| | Attention UNet | 7.85 | 11.1362+-0.0024 | 44.5546+-0.0024 | 0.9894+-0.0000 | 0.002881+-0.000000 | 0.000035+-0.000000 | 0.005924+-0.000002 | 1.388531+-0.001529 | -2.8484+-0.0096 | 0.271811+-0.000078 | 11.3208+-0.0025 | 6.901314+-0.012339 | -16.7698+-0.0152 | 3.179268+-0.003148 | -10.0299+-0.0084 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.313669+-0.000186 | 10.1016+-0.0052 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.239186+-0.000133 | 12.4375+-0.0046 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.231816+-0.000084 | 12.7057+-0.0035 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.011722+-0.002298 | -5.8902+-0.0083 | |
| | DDPM | 33.30 | 7.3520+-0.1258 | 40.7704+-0.1258 | 0.9704+-0.0008 | 0.006917+-0.000085 | 0.000087+-0.000003 | 0.009235+-0.000140 | 6.400374+-0.083429 | -15.9559+-0.1142 | 0.267865+-0.003680 | 11.5158+-0.1174 | 51.545130+-1.185846 | -33.8794+-0.2025 | 23.047020+-0.829319 | -26.9387+-0.2956 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.458381+-0.006855 | 6.9020+-0.1255 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.299931+-0.003971 | 10.5350+-0.1139 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.312099+-0.003603 | 10.1834+-0.1035 | 0.001317+-0.000000 | 73.5333-90.3333 | 4.238241+-0.076306 | -12.2105+-0.1462 | |
| | DnCNN | 0.56 | 11.9518+-0.0034 | 45.3702+-0.0034 | 0.9897+-0.0000 | 0.003337+-0.000001 | 0.000029+-0.000000 | 0.005394+-0.000002 | 2.139022+-0.000466 | -6.5928+-0.0021 | 0.232244+-0.000112 | 12.6831+-0.0042 | 13.817607+-0.005660 | -22.8012+-0.0034 | 6.403740+-0.003271 | -16.1159+-0.0043 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.269423+-0.000325 | 11.4154+-0.0105 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.230354+-0.000210 | 12.7601+-0.0076 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.211891+-0.000061 | 13.4842+-0.0028 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.749333+-0.002662 | -4.6522+-0.0117 | |
| | ResUNet | 8.11 | 9.7121+-0.0008 | 43.1305+-0.0008 | 0.9868+-0.0000 | 0.004095+-0.000000 | 0.000049+-0.000000 | 0.006980+-0.000000 | 2.710213+-0.000282 | -8.6483+-0.0009 | 0.300704+-0.000028 | 10.4445+-0.0009 | 18.291564+-0.005380 | -25.2383+-0.0025 | 8.421791+-0.001544 | -18.4938+-0.0015 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.361138+-0.000298 | 8.8818+-0.0068 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.258261+-0.000021 | 11.7699+-0.0011 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.258926+-0.000258 | 11.7461+-0.0084 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.724224+-0.001661 | -8.4668+-0.0051 | |
| | UNet | 7.76 | 10.5129+-0.0048 | 43.9313+-0.0048 | 0.9847+-0.0000 | 0.003534+-0.000002 | 0.000040+-0.000000 | 0.006361+-0.000003 | 1.862257+-0.001138 | -5.3904+-0.0051 | 0.285767+-0.000168 | 10.8867+-0.0049 | 11.745703+-0.009011 | -21.3908+-0.0068 | 5.497729+-0.002016 | -14.7914+-0.0029 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.295392+-0.000340 | 10.6085+-0.0097 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.298379+-0.000381 | 10.5175+-0.0108 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.250014+-0.000159 | 12.0534+-0.0055 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.658566+-0.001730 | -4.2297+-0.0092 | |
|
|
| #### SNR 5 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | 5.0000+-0.0015 | 38.4184+-0.0015 | 0.9528+-0.0000 | 0.009587+-0.000002 | 0.000145+-0.000000 | 0.012015+-0.000002 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.582355+-0.000039 | 4.7290+-0.0006 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.363760+-0.000081 | 8.7945+-0.0019 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.362912+-0.000104 | 8.8108+-0.0025 | 0.001317+-0.000000 | 73.5333-90.3333 | 5.996320+-0.004431 | -15.2465+-0.0056 | |
| | SCRN | 0.43 | 17.7735+-0.0044 | 51.1919+-0.0044 | 0.9970+-0.0000 | 0.001543+-0.000001 | 0.000008+-0.000000 | 0.002759+-0.000001 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.145397+-0.000006 | 16.7812+-0.0006 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.117155+-0.000016 | 18.6312+-0.0011 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.121608+-0.000095 | 18.3079+-0.0070 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.776633+-0.000326 | 2.2362+-0.0035 | |
| | Attention UNet | 7.85 | 12.3464+-0.0054 | 45.7647+-0.0053 | 0.9926+-0.0000 | 0.002436+-0.000001 | 0.000027+-0.000000 | 0.005154+-0.000003 | 1.302140+-0.000202 | -2.2909+-0.0012 | 0.235823+-0.000151 | 12.5582+-0.0056 | 6.489683+-0.001444 | -16.2294+-0.0022 | 2.849174+-0.001911 | -9.0745+-0.0057 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.273493+-0.000307 | 11.3198+-0.0095 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.198473+-0.000110 | 14.0576+-0.0047 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.194307+-0.000077 | 14.2399+-0.0036 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.905562+-0.001105 | -5.4070+-0.0026 | |
| | DDPM | 33.30 | 7.6850+-0.1814 | 41.1034+-0.1814 | 0.9725+-0.0011 | 0.006681+-0.000118 | 0.000081+-0.000004 | 0.008914+-0.000193 | 6.443338+-0.098793 | -15.9941+-0.1425 | 0.238385+-0.004719 | 12.5798+-0.1693 | 51.770507+-1.471348 | -33.8796+-0.2459 | 23.298755+-0.920540 | -26.9985+-0.3178 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.439799+-0.009433 | 7.2924+-0.1815 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.279271+-0.005615 | 11.1908+-0.1729 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.287681+-0.005280 | 10.9234+-0.1639 | 0.001317+-0.000000 | 73.5333-90.3333 | 4.253336+-0.100793 | -12.2218+-0.1919 | |
| | DnCNN | 0.56 | 15.1037+-0.0044 | 48.5221+-0.0044 | 0.9949+-0.0000 | 0.002262+-0.000001 | 0.000014+-0.000000 | 0.003753+-0.000002 | 1.446875+-0.000322 | -3.1992+-0.0019 | 0.163963+-0.000095 | 15.7072+-0.0050 | 8.230538+-0.004905 | -18.2988+-0.0053 | 3.831710+-0.001966 | -11.6548+-0.0046 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.188635+-0.000053 | 14.5166+-0.0021 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.153169+-0.000161 | 16.3053+-0.0090 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.145783+-0.000055 | 16.7324+-0.0035 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.304467+-0.001570 | -2.1151+-0.0091 | |
| | ResUNet | 8.11 | 10.8324+-0.0010 | 44.2508+-0.0010 | 0.9910+-0.0000 | 0.003324+-0.000000 | 0.000038+-0.000000 | 0.006137+-0.000001 | 2.112371+-0.000957 | -6.4850+-0.0041 | 0.270219+-0.000031 | 11.3781+-0.0010 | 13.936359+-0.003499 | -22.8763+-0.0022 | 6.320756+-0.001966 | -16.0011+-0.0029 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.310006+-0.000186 | 10.2124+-0.0054 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.214654+-0.000097 | 13.3832+-0.0039 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.218305+-0.000050 | 13.2314+-0.0019 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.720500+-0.002350 | -8.4605+-0.0080 | |
| | UNet | 7.76 | 12.7151+-0.0046 | 46.1335+-0.0046 | 0.9911+-0.0000 | 0.002776+-0.000001 | 0.000024+-0.000000 | 0.004937+-0.000003 | 1.481680+-0.000344 | -3.4066+-0.0018 | 0.221581+-0.000125 | 13.0955+-0.0048 | 9.064477+-0.003318 | -19.1393+-0.0034 | 4.239337+-0.001748 | -12.5320+-0.0035 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.239064+-0.000116 | 12.4555+-0.0039 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.215258+-0.000339 | 13.3532+-0.0133 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.191701+-0.000072 | 14.3603+-0.0033 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.533126+-0.002486 | -3.5443+-0.0129 | |
|
|
| ### Poisson Noise |
|
|
| #### SNR -5 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | -4.9986+-0.0015 | 28.4198+-0.0015 | 0.6663+-0.0001 | 0.030308+-0.000004 | 0.001447+-0.000000 | 0.037988+-0.000007 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 1.841246+-0.000500 | -5.2697+-0.0024 | 0.283209+-0.000000 | 6.3333-23.1333 | 1.149769+-0.000100 | -1.2014+-0.0009 | 0.425271+-0.000000 | 23.1333-48.3333 | 1.147469+-0.000565 | -1.1882+-0.0043 | 0.001317+-0.000000 | 73.5333-90.3333 | 18.965560+-0.004406 | -25.2484+-0.0018 | |
| | SCRN | 0.43 | 12.7233+-0.0070 | 46.1417+-0.0070 | 0.9911+-0.0000 | 0.002416+-0.000001 | 0.000024+-0.000000 | 0.004935+-0.000004 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.247088+-0.000065 | 12.1678+-0.0032 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.225437+-0.000589 | 12.9457+-0.0227 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.222882+-0.000124 | 13.0465+-0.0051 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.959926+-0.001372 | 0.3834+-0.0113 | |
| | Attention UNet | 7.85 | 9.2677+-0.0050 | 42.6861+-0.0050 | 0.9841+-0.0000 | 0.003340+-0.000001 | 0.000054+-0.000000 | 0.007347+-0.000004 | 1.457137+-0.003146 | -3.2655+-0.0188 | 0.338279+-0.000204 | 9.4182+-0.0053 | 10.006613+-0.029775 | -19.9800+-0.0254 | 3.113864+-0.005793 | -9.8431+-0.0157 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.381022+-0.000305 | 8.4090+-0.0067 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.316770+-0.000630 | 9.9933+-0.0171 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.299608+-0.000175 | 10.4757+-0.0051 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.009310+-0.003554 | -5.8739+-0.0114 | |
| | DDPM | 33.30 | 6.8701+-0.2412 | 40.2885+-0.2412 | 0.9656+-0.0015 | 0.007193+-0.000200 | 0.000096+-0.000005 | 0.009725+-0.000274 | 6.228295+-0.163362 | -15.7623+-0.2347 | 0.316087+-0.005547 | 10.0376+-0.1516 | 49.867653+-2.345451 | -33.6700+-0.4037 | 22.349018+-1.024055 | -26.7452+-0.3855 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.480265+-0.016030 | 6.4594+-0.2890 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.341868+-0.010202 | 9.3654+-0.2595 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.348159+-0.009248 | 9.2047+-0.2314 | 0.001317+-0.000000 | 73.5333-90.3333 | 4.089157+-0.171922 | -11.9225+-0.3729 | |
| | DnCNN | 0.56 | 9.1529+-0.0032 | 42.5713+-0.0032 | 0.9817+-0.0000 | 0.004577+-0.000001 | 0.000056+-0.000000 | 0.007444+-0.000003 | 2.963686+-0.002757 | -9.4231+-0.0081 | 0.318671+-0.000195 | 9.9353+-0.0053 | 20.184007+-0.006813 | -26.0929+-0.0025 | 9.310510+-0.014889 | -19.3658+-0.0138 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.362543+-0.000170 | 8.8329+-0.0045 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.327873+-0.000558 | 9.6921+-0.0147 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.294731+-0.000087 | 10.6183+-0.0027 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.304461+-0.000964 | -7.0340+-0.0028 | |
| | ResUNet | 8.11 | 8.0629+-0.0008 | 41.4812+-0.0008 | 0.9801+-0.0000 | 0.005003+-0.000001 | 0.000071+-0.000000 | 0.008439+-0.000001 | 3.283250+-0.001270 | -10.3128+-0.0034 | 0.362541+-0.000058 | 8.8179+-0.0015 | 22.618869+-0.015280 | -27.0822+-0.0056 | 10.436372+-0.009197 | -20.3564+-0.0077 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.439369+-0.000187 | 7.1704+-0.0042 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.333929+-0.000445 | 9.5405+-0.0117 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.322530+-0.000288 | 9.8364+-0.0081 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.927225+-0.003374 | -9.0763+-0.0105 | |
| | UNet | 7.76 | 8.9857+-0.0031 | 42.4041+-0.0031 | 0.9808+-0.0000 | 0.004256+-0.000000 | 0.000058+-0.000000 | 0.007587+-0.000003 | 2.415076+-0.002099 | -7.6446+-0.0074 | 0.337022+-0.000162 | 9.4499+-0.0042 | 15.838296+-0.020752 | -23.9837+-0.0112 | 7.364007+-0.012769 | -17.3268+-0.0150 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.369695+-0.000207 | 8.6562+-0.0048 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.346966+-0.000291 | 9.2024+-0.0073 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.307719+-0.000289 | 10.2453+-0.0085 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.854940+-0.000681 | -5.2019+-0.0030 | |
|
|
| #### SNR 0 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | 0.0023+-0.0012 | 33.4206+-0.0012 | 0.8645+-0.0000 | 0.017041+-0.000002 | 0.000458+-0.000000 | 0.021360+-0.000003 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 1.035247+-0.000049 | -0.2683+-0.0004 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.646679+-0.000100 | 3.7971+-0.0014 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.645245+-0.000461 | 3.8120+-0.0062 | 0.001317+-0.000000 | 73.5333-90.3333 | 10.666173+-0.001591 | -20.2493+-0.0019 | |
| | SCRN | 0.43 | 15.1084+-0.0042 | 48.5268+-0.0043 | 0.9946+-0.0000 | 0.001978+-0.000001 | 0.000014+-0.000000 | 0.003750+-0.000002 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.192670+-0.000073 | 14.3323+-0.0026 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.165939+-0.000231 | 15.6068+-0.0121 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.168089+-0.000002 | 15.4970+-0.0001 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.878353+-0.001559 | 1.1562+-0.0151 | |
| | Attention UNet | 7.85 | 11.1553+-0.0062 | 44.5737+-0.0062 | 0.9893+-0.0000 | 0.002898+-0.000001 | 0.000035+-0.000000 | 0.005911+-0.000004 | 1.390313+-0.000376 | -2.8592+-0.0024 | 0.271191+-0.000200 | 11.3407+-0.0065 | 6.832799+-0.013773 | -16.6845+-0.0180 | 3.183770+-0.002159 | -10.0437+-0.0061 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.311212+-0.000090 | 10.1706+-0.0021 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.241095+-0.000265 | 12.3663+-0.0099 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.232303+-0.000295 | 12.6878+-0.0110 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.948963+-0.002379 | -5.6070+-0.0092 | |
| | DDPM | 33.30 | 7.0527+-0.6239 | 40.4711+-0.6239 | 0.9676+-0.0048 | 0.007126+-0.000446 | 0.000095+-0.000016 | 0.009612+-0.000744 | 6.653783+-0.446833 | -16.2410+-0.5495 | 0.276525+-0.017426 | 11.2760+-0.5134 | 53.515679+-4.622386 | -34.1060+-0.6560 | 24.062672+-2.095657 | -27.2173+-0.6063 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.475384+-0.035410 | 6.6314+-0.6020 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.312405+-0.023336 | 10.2214+-0.6120 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.322814+-0.021756 | 9.9253+-0.5612 | 0.001317+-0.000000 | 73.5333-90.3333 | 4.417432+-0.370473 | -12.5098+-0.6560 | |
| | DnCNN | 0.56 | 11.9815+-0.0031 | 45.3999+-0.0031 | 0.9897+-0.0000 | 0.003295+-0.000001 | 0.000029+-0.000000 | 0.005376+-0.000002 | 2.079791+-0.000670 | -6.3499+-0.0029 | 0.232617+-0.000107 | 12.6689+-0.0040 | 13.344696+-0.009765 | -22.4992+-0.0063 | 6.179797+-0.002493 | -15.8068+-0.0033 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.266328+-0.000111 | 11.5152+-0.0037 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.230965+-0.000154 | 12.7369+-0.0059 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.211589+-0.000091 | 13.4965+-0.0039 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.752735+-0.000788 | -4.6601+-0.0031 | |
| | ResUNet | 8.11 | 9.6522+-0.0017 | 43.0706+-0.0017 | 0.9866+-0.0000 | 0.004132+-0.000001 | 0.000049+-0.000000 | 0.007028+-0.000002 | 2.740937+-0.000949 | -8.7459+-0.0027 | 0.302456+-0.000073 | 10.3936+-0.0022 | 18.531887+-0.013041 | -25.3514+-0.0060 | 8.541418+-0.001232 | -18.6162+-0.0013 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.362209+-0.000080 | 8.8562+-0.0018 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.261251+-0.000237 | 11.6697+-0.0082 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.259974+-0.000213 | 11.7098+-0.0073 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.777232+-0.001871 | -8.6346+-0.0041 | |
| | UNet | 7.76 | 10.2685+-0.0045 | 43.6869+-0.0045 | 0.9836+-0.0000 | 0.003591+-0.000001 | 0.000043+-0.000000 | 0.006543+-0.000003 | 1.889969+-0.000705 | -5.5192+-0.0033 | 0.294167+-0.000159 | 10.6331+-0.0049 | 11.981634+-0.005965 | -21.5637+-0.0042 | 5.589865+-0.005678 | -14.9357+-0.0088 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.302042+-0.000172 | 10.4159+-0.0051 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.306526+-0.000292 | 10.2817+-0.0086 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.251918+-0.000122 | 11.9870+-0.0046 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.700620+-0.001753 | -4.4417+-0.0097 | |
|
|
| #### SNR 5 dB |
|
|
| | Method | Parameters (M) | SNR | PSNR | SSIM | MAE | MSE | RMSE | EB_WSE_MEDIUM_40_70_NE | EB_WSE_MEDIUM_40_70_SNR | EB_WSE_STRONG_70_100_NE | EB_WSE_STRONG_70_100_SNR | EB_WSE_VERY_WEAK_5_20_NE | EB_WSE_VERY_WEAK_5_20_SNR | EB_WSE_WEAK_20_40_NE | EB_WSE_WEAK_20_40_SNR | FB_FRE_HIGH_ENERGY_RATIO | FB_FRE_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_HIGH_NE | FB_FRE_HIGH_SNR | FB_FRE_LOW_ENERGY_RATIO | FB_FRE_LOW_FREQUENCY_RANGE_HZ | FB_FRE_LOW_NE | FB_FRE_LOW_SNR | FB_FRE_MID_ENERGY_RATIO | FB_FRE_MID_FREQUENCY_RANGE_HZ | FB_FRE_MID_NE | FB_FRE_MID_SNR | FB_FRE_VERY_HIGH_ENERGY_RATIO | FB_FRE_VERY_HIGH_FREQUENCY_RANGE_HZ | FB_FRE_VERY_HIGH_NE | FB_FRE_VERY_HIGH_SNR | |
| |---|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:|:---:| |
| | Raw (noisy) | - | 5.0013+-0.0005 | 38.4197+-0.0005 | 0.9528+-0.0000 | 0.009585+-0.000001 | 0.000145+-0.000000 | 0.012013+-0.000001 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.582273+-0.000219 | 4.7300+-0.0031 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.363669+-0.000022 | 8.7967+-0.0007 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.362916+-0.000190 | 8.8104+-0.0045 | 0.001317+-0.000000 | 73.5333-90.3333 | 5.996074+-0.000881 | -15.2461+-0.0005 | |
| | SCRN | 0.43 | 17.7711+-0.0037 | 51.1895+-0.0037 | 0.9970+-0.0000 | 0.001536+-0.000001 | 0.000008+-0.000000 | 0.002760+-0.000001 | - | - | - | - | - | - | - | - | 0.168114+-0.000000 | 48.3333-73.5333 | 0.145232+-0.000050 | 16.7896+-0.0031 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.117670+-0.000020 | 18.5924+-0.0015 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.121606+-0.000104 | 18.3078+-0.0073 | 0.001317+-0.000000 | 73.5333-90.3333 | 0.775278+-0.000197 | 2.2525+-0.0020 | |
| | Attention UNet | 7.85 | 12.6766+-0.0026 | 46.0950+-0.0026 | 0.9927+-0.0000 | 0.002410+-0.000000 | 0.000025+-0.000000 | 0.004963+-0.000002 | 1.269969+-0.000532 | -2.0736+-0.0038 | 0.226817+-0.000076 | 12.8987+-0.0029 | 6.554225+-0.009750 | -16.3159+-0.0124 | 2.798576+-0.001532 | -8.9212+-0.0049 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.266124+-0.000058 | 11.5615+-0.0020 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.193572+-0.000061 | 14.2755+-0.0031 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.184649+-0.000163 | 14.6821+-0.0076 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.804460+-0.001932 | -4.9281+-0.0089 | |
| | DDPM | 33.30 | 7.7837+-0.1844 | 41.2021+-0.1844 | 0.9731+-0.0010 | 0.006599+-0.000113 | 0.000080+-0.000004 | 0.008816+-0.000195 | 6.370634+-0.085031 | -15.8910+-0.1299 | 0.235900+-0.005620 | 12.6722+-0.2013 | 51.105551+-1.416591 | -33.7590+-0.2365 | 23.034157+-0.819724 | -26.8922+-0.2705 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.435121+-0.009157 | 7.3876+-0.1786 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.276556+-0.005804 | 11.2774+-0.1790 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.284865+-0.005449 | 11.0101+-0.1739 | 0.001317+-0.000000 | 73.5333-90.3333 | 4.204208+-0.103543 | -12.1174+-0.1917 | |
| | DnCNN | 0.56 | 14.9594+-0.0020 | 48.3778+-0.0020 | 0.9947+-0.0000 | 0.002342+-0.000000 | 0.000015+-0.000000 | 0.003815+-0.000001 | 1.522983+-0.000381 | -3.6445+-0.0022 | 0.165433+-0.000050 | 15.6299+-0.0027 | 8.890889+-0.004972 | -18.9705+-0.0049 | 4.179185+-0.003171 | -12.4093+-0.0066 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.191927+-0.000064 | 14.3658+-0.0032 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.156217+-0.000152 | 16.1340+-0.0084 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.147813+-0.000084 | 16.6122+-0.0049 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.332505+-0.001006 | -2.3057+-0.0060 | |
| | ResUNet | 8.11 | 10.8216+-0.0028 | 44.2400+-0.0028 | 0.9910+-0.0000 | 0.003328+-0.000001 | 0.000038+-0.000000 | 0.006145+-0.000002 | 2.117962+-0.000157 | -6.5076+-0.0007 | 0.270441+-0.000104 | 11.3697+-0.0032 | 13.996343+-0.003306 | -22.9134+-0.0018 | 6.346291+-0.000121 | -16.0362+-0.0005 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.312489+-0.000390 | 10.1357+-0.0105 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.213235+-0.000057 | 13.4415+-0.0022 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.217869+-0.000157 | 13.2491+-0.0063 | 0.001317+-0.000000 | 73.5333-90.3333 | 2.743843+-0.000951 | -8.5181+-0.0040 | |
| | UNet | 7.76 | 12.6735+-0.0009 | 46.0919+-0.0009 | 0.9909+-0.0000 | 0.002779+-0.000000 | 0.000025+-0.000000 | 0.004961+-0.000001 | 1.481825+-0.000701 | -3.4074+-0.0040 | 0.222778+-0.000029 | 13.0481+-0.0011 | 9.008784+-0.003577 | -19.0862+-0.0034 | 4.219501+-0.002957 | -12.4921+-0.0066 | 0.168114+-0.000000 | 48.3333-73.5333 | 0.239523+-0.000217 | 12.4398+-0.0074 | 0.283209+-0.000000 | 6.3333-23.1333 | 0.216420+-0.000178 | 13.3066+-0.0068 | 0.425271+-0.000000 | 23.1333-48.3333 | 0.191949+-0.000036 | 14.3481+-0.0016 | 0.001317+-0.000000 | 73.5333-90.3333 | 1.547839+-0.002906 | -3.6236+-0.0174 | |
|
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| "ddpm": { |
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| "eb_wse_medium_40_70_snr": { |
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| "eb_wse_weak_20_40_ne": { |
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| "eb_wse_weak_20_40_snr": { |
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| 73.5333 |
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| "fb_fre_high_ne": { |
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| "fb_fre_mid_ne": { |
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| "fb_fre_mid_snr": { |
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| "fb_fre_very_high_energy_ratio": { |
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| "fb_fre_very_high_frequency_range_hz": [ |
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| "fb_fre_very_high_ne": { |
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| "mae": { |
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| "mse": { |
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| "psnr": { |
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| "rmse": { |
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| "snr": { |
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| "SCRN": { |
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| }, |
| "fb_fre_high_frequency_range_hz": [ |
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| 73.5333 |
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| "fb_fre_high_ne": { |
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| 23.1333 |
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| "fb_fre_mid_frequency_range_hz": [ |
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| 48.3333 |
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| "fb_fre_mid_ne": { |
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| "fb_fre_mid_snr": { |
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| "fb_fre_very_high_energy_ratio": { |
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| }, |
| "fb_fre_very_high_frequency_range_hz": [ |
| 73.5333, |
| 90.3333 |
| ], |
| "fb_fre_very_high_ne": { |
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| "mae": { |
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| }, |
| "mse": { |
| "mean": 8e-06, |
| "std": 0.0 |
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| "psnr": { |
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| }, |
| "rmse": { |
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| }, |
| "snr": { |
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| }, |
| "ssim": { |
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| "std": 0.0 |
| } |
| } |
| } |
| } |
| } |
| }, |
| "num_params": { |
| "unet": 7760000, |
| "dncnn": 560000, |
| "res_unet": 8110000, |
| "atten_unet": 7850000, |
| "ddpm": 33300000, |
| "SCRN": 430000 |
| } |
| } |
| AUTO_RESULTS_DATA_END --> |
| <!-- AUTO_RESULTS_END --> |
