# D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images

arXiv: https://arxiv.org/abs/1601.04149 · HTML: https://mlsanity.com/paper/1601.04149/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jan 2016.

## Built on by (most cited first, 22 of 22)

- [Learning to Optimize: A Primer and A Benchmark](https://mlsanity.com/paper/2103.12828/) (2021)
- [Lightweight Adaptive Feature De-drifting for Compressed Image Classification](https://mlsanity.com/paper/2401.01724/) (2024)
- [Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution](https://mlsanity.com/paper/2208.03012/) (2022)
- [HST: Hierarchical Swin Transformer for Compressed Image Super-resolution](https://mlsanity.com/paper/2208.09885/) (2022)
- [PromptCIR: Blind Compressed Image Restoration with Prompt Learning](https://mlsanity.com/paper/2404.17433/) (2024)
- [Attention-guided Image Compression by Deep Reconstruction of Compressive Sensed Saliency Skeleton](https://mlsanity.com/paper/2103.15368/) (2021)
- [Functional Neural Networks for Parametric Image Restoration Problems](https://mlsanity.com/paper/2111.00361/) (2021)
- [High-Perceptual Quality JPEG Decoding via Posterior Sampling](https://mlsanity.com/paper/2211.11827/) (2022)
- [Multi-view Self-supervised Disentanglement for General Image Denoising](https://mlsanity.com/paper/2309.05049/) (2023)
- [Real-time 6K Image Rescaling with Rate-distortion Optimization](https://mlsanity.com/paper/2304.01064/) (2023)
- [Perceptual Quality Improvement in Videoconferencing using Keyframes-based GAN](https://mlsanity.com/paper/2311.04263/) (2023)
- [Zooming SlowMo: An Efficient One-Stage Framework for Space-Time Video Super-Resolution](https://mlsanity.com/paper/2104.07473/) (2021)
- [Sensitivity Decouple Learning for Image Compression Artifacts Reduction](https://mlsanity.com/paper/2405.09291/) (2024)
- [Leveraging Bitstream Metadata for Fast, Accurate, Generalized Compressed Video Quality Enhancement](https://mlsanity.com/paper/2202.00011/) (2022)
- [DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus](https://mlsanity.com/paper/2211.10984/) (2022)
- [LADDER: Multi-objective Backdoor Attack via Evolutionary Algorithm](https://mlsanity.com/paper/2411.19075/) (2024)
- [A Design Space Study for LISTA and Beyond](https://mlsanity.com/paper/2104.04110/) (2021)
- [Learning Spatiotemporal Frequency-Transformer for Low-Quality Video Super-Resolution](https://mlsanity.com/paper/2212.14046/) (2022)
- [Enhancing Quality of Compressed Images by Mitigating Enhancement Bias Towards Compression Domain](https://mlsanity.com/paper/2402.17200/) (2024)
- [Uncover Treasures in DCT: Advancing JPEG Quality Enhancement by Exploiting Latent Correlations](https://mlsanity.com/paper/2506.21171/) (2025)
- [Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.11556/) (2024)
- [MDFI: A Multi-Domain Features Integration for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2608.21495/) (2026)

## Abstract (arXiv)

In this paper, we design a Deep Dual-Domain (D3) based fast restoration model to remove artifacts of JPEG compressed images. It leverages the large learning capacity of deep networks, as well as the problem-specific expertise that was hardly incorporated in the past design of deep architectures. For the latter, we take into consideration both the prior knowledge of the JPEG compression scheme, and the successful practice of the sparsity-based dual-domain approach. We further design the One-Step Sparse Inference (1-SI) module, as an efficient and light-weighted feed-forward approximation of sparse coding. Extensive experiments verify the superiority of the proposed D3 model over several state-of-the-art methods. Specifically, our best model is capable of outperforming the latest deep model for around 1 dB in PSNR, and is 30 times faster.