# Implicit Dual-domain Convolutional Network for Robust Color Image Compression Artifact Reduction

arXiv: https://arxiv.org/abs/1810.08042 · HTML: https://mlsanity.com/paper/1810.08042/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2018.

## Built on by (most cited first, 20 of 20)

- [Towards Flexible Blind JPEG Artifacts Removal](https://mlsanity.com/paper/2109.14573/) (2021)
- [Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration](https://mlsanity.com/paper/2209.11345/) (2022)
- [SDPL: Shifting-Dense Partition Learning for UAV-View Geo-Localization](https://mlsanity.com/paper/2403.04172/) (2024)
- [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)
- [Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal](https://mlsanity.com/paper/2502.09873/) (2025)
- [JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients](https://mlsanity.com/paper/2404.05558/) (2024)
- [FPANet: Frequency-based Video Demoireing using Frame-level Post Alignment](https://mlsanity.com/paper/2301.07330/) (2023)
- [Learning Parallax Transformer Network for Stereo Image JPEG Artifacts Removal](https://mlsanity.com/paper/2207.07335/) (2022)
- [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)
- [JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer](https://mlsanity.com/paper/2308.09110/) (2023)
- [OAPT: Offset-Aware Partition Transformer for Double JPEG Artifacts Removal](https://mlsanity.com/paper/2408.11480/) (2024)
- [Capturing Stable HDR Videos Using a Dual-Camera System](https://mlsanity.com/paper/2507.06593/) (2025)
- [ConStyle v2: A Strong Prompter for All-in-One Image Restoration](https://mlsanity.com/paper/2406.18242/) (2024)
- [Multi-Modality Deep Network for JPEG Artifacts Reduction](https://mlsanity.com/paper/2305.02760/) (2023)
- [Hierarchical Frequency-based Upsampling and Refining for Compressed Video Quality Enhancement](https://mlsanity.com/paper/2403.11556/) (2024)
- [EHPE: A Segmented Architecture for Enhanced Hand Pose Estimation](https://mlsanity.com/paper/2507.09560/) (2025)
- [Subjective and Objective Quality Assessment of Banding Artifacts on Compressed Videos](https://mlsanity.com/paper/2508.08700/) (2025)
- [JSGS: JPEG State-Guided Supervision for 3D Gaussian Splatting from Mixed-Quality Views](https://mlsanity.com/paper/2608.08659/) (2026)

## Abstract (arXiv)

Several dual-domain convolutional neural network-based methods show outstanding performance in reducing image compression artifacts. However, they suffer from handling color images because the compression processes for gray-scale and color images are completely different. Moreover, these methods train a specific model for each compression quality and require multiple models to achieve different compression qualities. To address these problems, we proposed an implicit dual-domain convolutional network (IDCN) with the pixel position labeling map and the quantization tables as inputs. Specifically, we proposed an extractor-corrector framework-based dual-domain correction unit (DCU) as the basic component to formulate the IDCN. A dense block was introduced to improve the performance of extractor in DRU. The implicit dual-domain translation allows the IDCN to handle color images with the discrete cosine transform (DCT)-domain priors. A flexible version of IDCN (IDCN-f) was developed to handle a wide range of compression qualities. Experiments for both objective and subjective evaluations on benchmark datasets show that IDCN is superior to the state-of-the-art methods and IDCN-f exhibits excellent abilities to handle a wide range of compression qualities with little performance sacrifice and demonstrates great potential for practical applications.