mlsanityData to 14 Oct 2026

A low-complexity method for efficient depth-guided image deblurring

arXiv 2601.03924 ↗First posted 7 Jan 2026eess.IV cs.CVcited by 0

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Abstract (arXiv)

Image deblurring is a challenging problem in imaging due to its highly ill-posed nature. Deep learning models have shown great success in tackling this problem but the quest for the best image quality has brought their computational complexity up, making them impractical on anything but powerful servers. Meanwhile, recent works have shown that mobile Lidars can provide complementary information in the form of depth maps that enhance deblurring quality. In this paper, we introduce a novel low-complexity neural network for depth-guided image deblurring. We show that the use of the wavelet transform to separate structural details and reduce spatial redundancy as well as efficient feature conditioning on the depth information are essential ingredients in developing a low-complexity model. Experimental results show competitive image quality against recent state-of-the-art models while reducing complexity by up to two orders of magnitude.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 26 of its 41 references that are in the map. First posted on arXiv 7 Jan 2026.

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26 of 41 references.

References · 26

  1. 2017Attention Is All You Needcited by 60,361
  2. 2020An Image is Worth 16x16 Words: Transformers for Image Recognition at Scalecited by 30,186
  3. 2021Restormer: Efficient Transformer for High-Resolution Image Restorationcited by 1,627
  4. 2020Pre-Trained Image Processing Transformercited by 893
  5. 2021Uformer: A General U-Shaped Transformer for Image Restorationcited by 866
  6. 2022Simple Baselines for Image Restorationcited by 708
  7. 2021ARKitScenes: A Diverse Real-World Dataset For 3D Indoor Scene Understanding Using Mobile RGB-D Datacited by 454
  8. 2017DeblurGAN: Blind Motion Deblurring Using Conditional Adversarial Networkscited by 367
  9. 2019DeblurGAN-v2: Deblurring (Orders-of-Magnitude) Faster and Bettercited by 362
  10. 2023Implicit Diffusion Models for Continuous Super-Resolutioncited by 218
  11. 2022Efficient Frequency Domain-based Transformers for High-Quality Image Deblurringcited by 165
  12. 2021Transformer for Single Image Super-Resolutioncited by 150
14 more
  1. 2022Stripformer: Strip Transformer for Fast Image Deblurringcited by 140
  2. 2021ClassSR: A General Framework to Accelerate Super-Resolution Networks by Data Characteristiccited by 81
  3. 2023Zero-Shot Noise2Noise: Efficient Image Denoising without any Datacited by 61
  4. 2024Denoising: A Powerful Building-Block for Imaging, Inverse Problems, and Machine Learningcited by 35
  5. 2022MSSNet: Multi-Scale-Stage Network for Single Image Deblurringcited by 31
  6. 2024CAMixerSR: Only Details Need More "Attention"cited by 30
  7. 2024AdaRevD: Adaptive Patch Exiting Reversible Decoder Pushes the Limit of Image Deblurringcited by 25
  8. 2023Efficient Multi-scale Network with Learnable Discrete Wavelet Transform for Blind Motion Deblurringcited by 24
  9. 2023SAM-Deblur: Let Segment Anything Boost Image Deblurringcited by 11
  10. 2024Rethinking Video Deblurring with Wavelet-Aware Dynamic Transformer and Diffusion Modelcited by 11
  11. 2023Revisiting Image Deblurring with an Efficient ConvNetcited by 9
  12. 2023Deformable Convolutions and LSTM-based Flexible Event Frame Fusion Network for Motion Deblurringcited by 3
  13. 2019Motion Deblurring with an Adaptive Networkcited by 1
  14. 2024Deep Lidar-guided Image Deblurringcited by 1

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