Deep Edge-Aware Post-Processing for JPEG Enhancement: CNN-Based Artifact Reduction and Image Quality Restoration

Abstract

Joint photographic experts group (JPEG) is one of the most widely used image compression standards, but its lossy nature often introduces visible artifacts such as blocking, ringing, and blurring, particularly at lower quality factors. These degradations significantly reduce perceptual quality and affect downstream computer vision tasks. To address these limitations, in this study work a CNN-based edge-aware artifact reduction framework (CNN-AR) is proposed that integrates an enhanced deep super-resolution (EDSR) backbone with a holistically nested edge detection (HED) guided loss. This design enforces both pixel fidelity and edge consistency, enabling superior artifact suppression while preserving fine structural details. Extensive experiments conducted on benchmark datasets (LIVE1, Kodak, Set14, Classic5, and CLIC) across quality factors 10–40 demonstrate the effectiveness of the proposed approach. Compared to state-ofthe-art models including ARCNN, DnCNN, and DPW-SDNet, the proposed method consistently achieves higher perceptual scores. On average, CNN-AR improves PSNR by +0.38 dB, Structural Similarity Index (SSIM) by +0.012, MS-SSIM by +0.009, and PSNR-B by +0.41 dB across datasets, shows its ability to deliver both numerically superior and visually sharper reconstructions.