mlsanityData to 14 Oct 2026

Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement (JUDE)

arXiv 2412.07527 ↗First posted 10 Dec 2024cs.CVcited by 4

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Abstract (arXiv)

Low-light and blurring issues are prevalent when capturing photos at night, often due to the use of long exposure to address dim environments. Addressing these joint problems can be challenging and error-prone if an end-to-end model is trained without incorporating an appropriate physical model. In this paper, we introduce JUDE, a Deep Joint Unrolling for Deblurring and Low-Light Image Enhancement, inspired by the image physical model. Based on Retinex theory and the blurring model, the low-light blurry input is iteratively deblurred and decomposed, producing sharp low-light reflectance and illuminance through an unrolling mechanism. Additionally, we incorporate various modules to estimate the initial blur kernel, enhance brightness, and eliminate noise in the final image. Comprehensive experiments on LOL-Blur and Real-LOL-Blur demonstrate that our method outperforms existing techniques both quantitatively and qualitatively.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Builds on 39 of its 58 references that are in the map. First posted on arXiv 10 Dec 2024.

Builds on

39 of 58 references.

References · 39

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27 more
  1. 2018Scale-recurrent Network for Deep Image Deblurringcited by 333
  2. 2021Rethinking Coarse-to-Fine Approach in Single Image Deblurringcited by 326
  3. 2020Retinex-inspired Unrolling with Cooperative Prior Architecture Search for Low-light Image Enhancementcited by 303
  4. 2019Blind Image Quality Assessment Using A Deep Bilinear Convolutional Neural Networkcited by 302
  5. 2022MAXIM: Multi-Axis MLP for Image Processingcited by 302
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  7. 2019ResUNet-a: a deep learning framework for semantic segmentation of remotely sensed datacited by 261
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  11. 2015LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancementcited by 224
  12. 2015Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restorationcited by 195
  13. 2022Ultra-High-Definition Low-Light Image Enhancement: A Benchmark and Transformer-Based Methodcited by 174
  14. 2022Efficient Frequency Domain-based Transformers for High-Quality Image Deblurringcited by 165
  15. 2021Low-Light Image and Video Enhancement Using Deep Learning: A Surveycited by 148
  16. 2023Diff-Retinex: Rethinking Low-light Image Enhancement with A Generative Diffusion Modelcited by 118
  17. 2022LEDNet: Joint Low-light Enhancement and Deblurring in the Darkcited by 96
  18. 2021Image Quality Assessment using Contrastive Learningcited by 95
  19. 2023ARNIQA: Learning Distortion Manifold for Image Quality Assessmentcited by 89
  20. 2023Hierarchical Integration Diffusion Model for Realistic Image Deblurringcited by 88
  21. 2021Intriguing Findings of Frequency Selection for Image Deblurringcited by 81
  22. 2022AdaInt: Learning Adaptive Intervals for 3D Lookup Tables on Real-time Image Enhancementcited by 70
  23. 2023FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Informationcited by 64
  24. 2021Adaptive Unfolding Total Variation Network for Low-Light Image Enhancementcited by 55
  25. 2021Progressive Joint Low-light Enhancement and Noise Removal for Raw Imagescited by 16
  26. 2013Sparsity Based Poisson Denoising with Dictionary Learningcited by 9
  27. 2023Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurringcited by 5

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025CompEvent: Complex-valued Event-RGB Fusion for Low-light Video Enhancement and Deblurringcited by 3
  2. 2026Bridging Robustness and Efficiency: Real-Time Low-Light Enhancement via Attention U-Net GANcited by 0
  3. 2026ErA: Error-Aware Deep Unrolling Network for Single Image Defocus Deblurringcited by 0
  4. 2026Leveraging Phase Information to Boost Unrolled Network Learning for Image Deblurringcited by 0