RECA: A Pipeline for Refinement of Compressed Artifacts in Image Super-Resolution Training

Training datasets for image super-resolution (SR) are often constructed from web images. However, these images are typically stored in JPEG format, introducing compression artifacts that degrade SR performance. To ensure data quality, conventional approaches discard severely compressed images. While this preserves image quality, it reduces data diversity, which in turn limits model performance. To address this issue, we propose RECA (Refinement of Compressed Artifacts), a novel pipeline that enables the reuse of JPEG images that would otherwise be excluded. RECA first detects heavily compressed images using a blockiness measure, then restores visual quality using a real-world diffusion-based SR model, and finally applies bicubic downsampling to suppress artifacts introduced during enhancement. This process allows the effective inclusion of compressed images in training without sacrificing data quality. Experimental results demonstrate that RECA consistently outperforms filtering-based baselines in terms of PSNR and SSIM. These findings highlight the effectiveness of RECA in expanding usable training data and improving SR model performance.