| --- |
| language: |
| - en |
| license: cc-by-nc-nd-4.0 |
| tags: |
| - image-to-image |
| - 16bit-reconstruction |
| - hdr |
| - custom-vision |
| datasets: |
| - custom-dataset-8bit-to-16bit |
| metrics: |
| - name: Median MAE |
| type: regression |
| value: 410 |
| - name: LPIPS (Alex) |
| type: perceptual |
| value: 0.044 |
| --- |
| |
| # 16bit-from-8bit Image Reconstruction Model |
|
|
| This model reconstructs **16-bit per channel images** from standard **8-bit input images**. It is trained on paired datasets and optimized to preserve color fidelity, structural consistency, and high-frequency detail. |
|
|
| - **Median MAE:** 410 |
| - **LPIPS (Alex):** ~0.044 (60-image evaluation) |
| - **Architecture Update:** Added Leaky ReLU |
| - **Training Resolution:** 256×256(46k Patches from Raw HDR images with 8,580 48bit synthetic images.) |
| - **Training Resolution:** 512×512 (Hand Selected Dataset 2k) |
| - **Training Resolution:** 1024×1024 (Hand Selected Dataset 500) |
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| --- |
|
|
| ## Dataset |
|
|
| - **Total images:** 54,580 |
| - **RAW patch images:** 46,000 @ 256x256 (~10 GB) |
| - **48-bit synthetic images:** 8,580 (~2 GB) |
|
|
| Addtional 10GB in Hand Selected RAW images, for the 512px and 1024px High Frequency Training Passes |
| --- |
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| ### MAE Distribution (8-bit → 16-bit reconstruction) |
|
|
| | MAE Range | Accuracy Comment | Percent (%) | |
| |------------ |--------------------------------------|------------ | |
| | ≥1000 | Occasionally visible in uniform areas | 1.06 | |
| | 600–1000 | Almost never visible | 10.03 | |
| | 400–600 | Fully imperceptible | 27.39 | |
| | 200–400 | Near perfect | 59.95 | |
| | ≤200 | Near exact scientific | 1.57 | |
|
|
| ### Perceptual Metrics (60-image test set) |
|
|
| | Metric | Result | Interpretation | |
| |-------------------|--------|----------------| |
| | LPIPS (Alex) | 0.044 | Low perceptual distance / high similarity | |
| | Gradient Energy | 0.088 → 0.108 | Preserved fine detail, slight sharpening | |
| | FFT Structure Score| 1.07 → 1.23 | Improved high-frequency retention | |
| | Histogram Continuity | 11.2 → 11.3 | Stable tonal distribution | |
|
|
| ## Interpretation Summary |
|
|
| - LPIPS values (~0.03–0.07 range) indicate **high perceptual similarity** |
| - Structural metrics (FFT + gradients) show **consistent detail reconstruction** |
| - Histogram stability indicates **no major tonal drift between bit-depth conversions** |
|
|
| --- |
|
|
| ## Intended Use |
|
|
| **Primary Use Cases** |
| - Reconstruction of 16-bit per channel images from 8-bit input |
| - JPG & GIF post-processing and enhancement |
| - Archival and art restoration workflows |
|
|
| **Not Intended For** |
| - Lossless scientific measurement or precision tasks |
| - Medical AI enhancement |
