🔧

Image Restoration - Real-World Blur Removal

🛠

Diffusion-based Deblurring

Removes motion blur from images using generative diffusion models with Stable Diffusion priors. Uses a Latent Kernel Prediction Network (LKPN) for robust real-world deblurring with iterative refinement. Achieves state-of-the-art results on both benchmark and real-world images with superior detail reconstruction.

What it does

DeblurDiff is a deep learning model that removes motion blur from photographs using generative diffusion models. It introduces a Latent Kernel Prediction Network (LKPN) that learns spatially variant kernels to guide sharp image restoration in latent space. The model co-trains with Stable Diffusion to leverage clear image priors while using Element-wise Adaptive Convolution (EAC) to preserve structural information from the blurry input.

Problem it solves

  • Remove motion blur from real-world photographs
  • Restore sharpness while preserving structural integrity
  • Handle complex, spatially variant blur patterns
  • Reconstruct realistic textures and fine details
  • Overcome limitations of pre-deblurred conditioning approaches
  • Generalize across different blur types and domains

Input/Output

Input:
Input: Blurred RGB image (any size)
Output:
Output: Sharp/deblurred RGB image (same size as input)
image

Accuracy & Speed

  • Performance:
    • Accuracy: Outperforms state-of-the-art methods on benchmark and real-world images
    • Training datasets: Synthetic blur-sharp pairs with real-world validation
    • Comparisons: Exceeds FFTformer, DBGAN, HiDiff, ResShift, ControlNet, PASD, and DiffBIR
  • Metrics:
    • Superior structural fidelity vs. competing methods
    • High-quality detail and texture reconstruction
    • Robust performance on unseen blur patterns

Technical Details

  • Architecture: Latent Kernel Prediction Network (LKPN) + Stable Diffusion
  • Core Innovation:
    • LKPN learns spatially variant kernels in latent space
    • Element-wise Adaptive Convolution (EAC) preserves structural information
    • Iterative kernel refinement using intermediate diffusion results
    • Co-training with conditional diffusion for robust guidance
  • Design Benefits:
    • Avoids dependency on pre-deblurred image quality
    • Better generalization across blur types than DRM-based methods
    • Preserves input structure while generating realistic details
    • Iterative refinement improves accuracy progressively
  • Framework & Dependencies:
    • Framework: PyTorch
    • Base model: Stable Diffusion (pre-trained)
    • Dependencies: See environment.yml in repository
    • GPU required for practical inference times
  • Training:
    • Approach: Co-training LKPN with conditional diffusion
    • Learning strategy: Iterative kernel estimation with diffusion feedback
    • Built upon: DiffBIR and DemystifyLocalViT codebases

Compliance & Provenance

Provider
Open-source
Provider type
Specialized
License
MIT
EU AI Act risk class
Minimal Risk
Art. 50 transparency
Not applicable
Region availability
Available globally
Training data summary
Pending — provider has not yet published per Art. 53(d)

For more on how we classify models and mark outputs, see our AI Policy.

Model Source