Compression Artifacts Removal Using Convolutional Neural Networks

2016/05/02 by Pavel Svoboda, Michal Hradiš, Svoboda, Pavel +5 · 1 citation
Computer Science · #Advanced Image Processing Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image and Signal Denoising Methods

paper · pdf · doi:10.48550/arxiv.1605.00366

openalex publication_date 2016/05/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

This paper shows that it is possible to train large and deep convolutional neural networks (CNN) for JPEG compression artifacts reduction, and that such networks can provide significantly better reconstruction quality compared to previously used smaller networks as well as to any other state-of-the-art methods. We were able to train networks with 8 layers in a single step and in relatively short time by combining residual learning, skip architecture, and symmetric weight initialization. We provide further insights into convolution networks for JPEG artifact reduction by evaluating three different objectives, generalization with respect to training dataset size, and generalization with respect to JPEG quality level.

Citations

• Deep Residual Learning for Image Recognition
• Accurate Image Super-Resolution Using Very Deep Convolutional Networks

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