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Compression Artifacts Reduction by a Deep Convolutional Network

arXiv 1504.06993 ↗First posted 27 Apr 2015cs.CVcited by 136

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Abstract (arXiv)

Lossy compression introduces complex compression artifacts, particularly the blocking artifacts, ringing effects and blurring. Existing algorithms either focus on removing blocking artifacts and produce blurred output, or restores sharpened images that are accompanied with ringing effects. Inspired by the deep convolutional networks (DCN) on super-resolution, we formulate a compact and efficient network for seamless attenuation of different compression artifacts. We also demonstrate that a deeper model can be effectively trained with the features learned in a shallow network. Following a similar "easy to hard" idea, we systematically investigate several practical transfer settings and show the effectiveness of transfer learning in low-level vision problems. Our method shows superior performance than the state-of-the-arts both on the benchmark datasets and the real-world use case (i.e. Twitter). In addition, we show that our method can be applied as pre-processing to facilitate other low-level vision routines when they take compressed images as input.

Key facts. Cited by 136 papers in the map (data through 7 Oct 2026). First posted on arXiv 27 Apr 2015.

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2021: 34; 2022: 29; 2023: 21; 2024: 21; 2025: 15; 2026: 16.

136 papers.

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  1. 2021SwinIR: Image Restoration Using Swin Transformercited by 1,869
  2. 2021Palette: Image-to-Image Diffusion Modelscited by 1,296
  3. 2024MambaIR: A Simple Baseline for Image Restoration with State-Space Modelcited by 349
  4. 2021Towards Real-World Blind Face Restoration with Generative Facial Priorcited by 347
  5. 2023DiffIR: Efficient Diffusion Model for Image Restorationcited by 253
  6. 2022Deep Generalized Unfolding Networks for Image Restorationcited by 119
  7. 2023Diffusion Models for Image Restoration and Enhancement: A Comprehensive Surveycited by 108
  8. 2022Cross Aggregation Transformer for Image Restorationcited by 92
  9. 2024VmambaIR: Visual State Space Model for Image Restorationcited by 90
  10. 2024MambaIRv2: Attentive State Space Restorationcited by 90
  11. 2023DR2: Diffusion-based Robust Degradation Remover for Blind Face Restorationcited by 79
  12. 2021Towards Flexible Blind JPEG Artifacts Removalcited by 76
38 more
  1. 2022Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restorationcited by 72
  2. 2023HAT: Hybrid Attention Transformer for Image Restorationcited by 64
  3. 2021HDRUNet: Single Image HDR Reconstruction with Denoising and Dequantizationcited by 58
  4. 2021NTIRE 2021 Challenge on Image Deblurringcited by 49
  5. 2021Dynamic Attentive Graph Learning for Image Restorationcited by 48
  6. 2024Lightweight Adaptive Feature De-drifting for Compressed Image Classificationcited by 46
  7. 2022JPEG Artifact Correction using Denoising Diffusion Restoration Modelscited by 45
  8. 2022Learning Dual Memory Dictionaries for Blind Face Restorationcited by 40
  9. 2021On Measuring and Controlling the Spectral Bias of the Deep Image Priorcited by 36
  10. 2023A Residual Diffusion Model for High Perceptual Quality Codec Augmentationcited by 33
  11. 2021NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Resultscited by 26
  12. 2022Variational Deep Image Restorationcited by 24
  13. 2021Wavelength-based Attributed Deep Neural Network for Underwater Image Restorationcited by 23
  14. 2022Basic Binary Convolution Unit for Binarized Image Restoration Networkcited by 22
  15. 2022HST: Hierarchical Swin Transformer for Compressed Image Super-resolutioncited by 20
  16. 2022AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Resultscited by 20
  17. 2023SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolutioncited by 18
  18. 2024Learning A Low-Level Vision Generalist via Visual Task Promptcited by 18
  19. 2022Deep Face Restoration: A Surveycited by 17
  20. 2023DiffBFR: Bootstrapping Diffusion Model Towards Blind Face Restorationcited by 17
  21. 2023Deep Equilibrium Diffusion Restoration with Parallel Samplingcited by 17
  22. 2024CPGA: Coding Priors-Guided Aggregation Network for Compressed Video Quality Enhancementcited by 17
  23. 2021COLA-Net: Collaborative Attention Network for Image Restorationcited by 16
  24. 2021Training a Task-Specific Image Reconstruction Losscited by 16
  25. 2021Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeletoncited by 16
  26. 2021Toward Interactive Modulation for Photo-Realistic Image Restorationcited by 15
  27. 2021Multi-modality Deep Restoration of Extremely Compressed Face Videoscited by 14
  28. 2021Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reductioncited by 14
  29. 2025Hybrid Agents for Image Restorationcited by 14
  30. 2021Attribute Artifacts Removal for Geometry-based Point Cloud Compressioncited by 13
  31. 2025Compression-Aware One-Step Diffusion Model for JPEG Artifact Removalcited by 13
  32. 2024Overcoming False Illusions in Real-World Face Restoration with Multi-Modal Guided Diffusion Modelcited by 12
  33. 2022Unsupervised Flow-Aligned Sequence-to-Sequence Learning for Video Restorationcited by 11
  34. 2023Toward DNN of LUTs: Learning Efficient Image Restoration with Multiple Look-Up Tablescited by 11
  35. 2021Exploiting Aliasing for Manga Restorationcited by 10
  36. 2022Blind Face Restoration: Benchmark Datasets and a Baseline Modelcited by 10
  37. 2021NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Dataset and Studycited by 9
  38. 2021EDPN: Enhanced Deep Pyramid Network for Blurry Image Restorationcited by 9

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