| --- |
| license: mit |
| tags: |
| - image-deblurring |
| - computer-vision |
| - onnx |
| - pytorch |
| - real-time |
| pipeline_tag: image-to-image |
| --- |
| |
| # RT-Focuser: Real-Time Lightweight Model for Edge-side Image Deblurring |
|
|
| Official PyTorch and ONNX release of RT-Focuser, a lightweight image deblurring model designed for real-time edge deployment. |
|
|
| ## Model Details |
|
|
| - Developed by: Zhuoyu Wu and collaborators |
| - Shared by: ReaganWu |
| - Model type: Image deblurring model |
| - Frameworks: PyTorch, ONNX |
| - License: MIT |
| - Paper: [RT-Focuser: A Real-Time Lightweight Model for Edge-side Image Deblurring](https://arxiv.org/abs/2512.21975) |
| - Code repository: [GitHub - ReaganWu/RT-Focuser](https://github.com/ReaganWu/RT-Focuser) |
|
|
| ## Model Description |
|
|
| RT-Focuser is a lightweight single-input single-output image deblurring model for real-time deployment on edge devices. It is designed to balance restoration quality and inference speed, with support for both PyTorch and ONNX inference. |
|
|
| Highlights: |
| - Lightweight: 5.85M parameters |
| - Efficient: 15.76 GMACs |
| - Real-time performance on desktop and mobile hardware |
| - Suitable for edge-side image restoration and streaming scenarios |
|
|
| ## Available Checkpoints |
|
|
| This repository provides: |
| - `Pretrained_Weights/GoPro_RT_Focuser_Standard_256.pth` |
| - `Pretrained_Weights/rt_focuser_wint8_afp16.onnx` |
| - `Pretrained_Weights/rt_focuser_wint8_afp32.onnx` |
| - `Pretrained_Weights/rt_focuser_wint8_aint8.onnx` |
|
|
| ## Intended Use |
|
|
| Direct use: |
| - Single-image motion deblurring |
| - Real-time restoration pipelines |
| - Edge/mobile/embedded deployment experiments |
| - ONNX-based inference benchmarking |
|
|
| Out-of-scope use: |
| - Medical or safety-critical decision making |
| - Forensic restoration requiring guaranteed fidelity |
| - General-purpose enhancement outside motion-deblur settings without validation |
|
|
| ## Training Data |
|
|
| The released model is trained and evaluated on the GoPro image deblurring dataset. |
|
|
| ## Performance |
|
|
| Reported results from the project README: |
|
|
| | Model | PSNR | SSIM | Params | GMACs | Time (s) | |
| | --- | --- | --- | --- | --- | --- | |
| | RT-Focuser | 30.67 | 0.9005 | 5.85M | 15.76 | 0.006 | |
|
|
| Additional reported deployment speed: |
| - iPhone 15 (CoreML): 146.72 FPS |
| - RTX 3090 (PyTorch CUDA): 154.42 FPS |
| - Intel Xeon CPU (OpenVINO): 22.74 FPS |
| - Intel Xeon CPU (ONNX Runtime): 14.95 FPS |
|
|
| ## Risks and Limitations |
|
|
| - Performance depends on blur type, resolution, and domain shift. |
| - The model is primarily presented for research and engineering use. |
| - Results outside the GoPro-style motion blur setting may degrade noticeably. |
| - Quantized ONNX checkpoints may trade image quality for speed depending on backend and hardware. |
|
|
| ## How to Use |
|
|
| ### PyTorch |
|
|
| ```python |
| import torch |
| from PIL import Image |
| import torchvision.transforms as transforms |
| from model.rt_focuser_model import RT_Focuser_Standard |
| |
| model = RT_Focuser_Standard() |
| checkpoint = torch.load( |
| "Pretrained_Weights/GoPro_RT_Focuser_Standard_256.pth", |
| map_location="cpu" |
| ) |
| model.load_state_dict(checkpoint, strict=True) |
| model.eval() |
| |
| transform = transforms.Compose([ |
| transforms.Resize((256, 256)), |
| transforms.ToTensor() |
| ]) |
| |
| image = Image.open("Sample/Blurry.png").convert("RGB") |
| input_tensor = transform(image).unsqueeze(0) |
| |
| with torch.no_grad(): |
| output = model(input_tensor) |
| |
