# DPW-SDNet: Dual Pixel-Wavelet Domain Deep CNNs for Soft Decoding of JPEG-Compressed Images

arXiv: https://arxiv.org/abs/1805.10558 · HTML: https://mlsanity.com/paper/1805.10558/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2018.

## Built on by (most cited first, 10 of 10)

- [Recursive Fusion and Deformable Spatiotemporal Attention for Video Compression Artifact Reduction](https://mlsanity.com/paper/2108.02110/) (2021)
- [End-to-end Transformer for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2210.13827/) (2022)
- [Sensitivity Decouple Learning for Image Compression Artifacts Reduction](https://mlsanity.com/paper/2405.09291/) (2024)
- [Boosting the Performance of Video Compression Artifact Reduction with Reference Frame Proposals and Frequency Domain Information](https://mlsanity.com/paper/2105.14962/) (2021)
- [Online Video Quality Enhancement with Spatial-Temporal Look-up Tables](https://mlsanity.com/paper/2311.13616/) (2023)
- [Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations](https://mlsanity.com/paper/2506.21171/) (2025)
- [Deep learning-based Edge-aware pre and post-processing methods for JPEG compressed images](https://mlsanity.com/paper/2104.04926/) (2021)
- [WaveNets: Wavelet Channel Attention Networks](https://mlsanity.com/paper/2211.02695/) (2022)
- [Lightweight Hybrid Video Compression Framework Using Reference-Guided Restoration Network](https://mlsanity.com/paper/2303.11592/) (2023)
- [Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.11556/) (2024)

## Abstract (arXiv)

JPEG is one of the widely used lossy compression methods. JPEG-compressed images usually suffer from compression artifacts including blocking and blurring, especially at low bit-rates. Soft decoding is an effective solution to improve the quality of compressed images without changing codec or introducing extra coding bits. Inspired by the excellent performance of the deep convolutional neural networks (CNNs) on both low-level and high-level computer vision problems, we develop a dual pixel-wavelet domain deep CNNs-based soft decoding network for JPEG-compressed images, namely DPW-SDNet. The pixel domain deep network takes the four downsampled versions of the compressed image to form a 4-channel input and outputs a pixel domain prediction, while the wavelet domain deep network uses the 1-level discrete wavelet transformation (DWT) coefficients to form a 4-channel input to produce a DWT domain prediction. The pixel domain and wavelet domain estimates are combined to generate the final soft decoded result. Experimental results demonstrate the superiority of the proposed DPW-SDNet over several state-of-the-art compression artifacts reduction algorithms.