MSERNet: a multi-scale edge-guided network for JPEG compression artifact restoration in retrospective ovarian ultrasound images

Introduction JPEG compression is widely used to store and transmit medical ultrasound images but can introduce artifacts that affect image quality and subsequent image analysis. This study proposes a Multi-Scale Edge Guided Restoration Network (MSERNet) for reducing JPEG compression artifacts while preserving tissue textures and anatomical boundaries in ultrasound images. Methods An ethically approved, anonymized private ovarian ultrasound dataset was curated and used to generate JPEG-compressed and recompressed images at multiple quality levels. MSERNet integrates multi-scale feature extraction, adaptive feature fusion, and edge refinement within a residual learning framework. The model was evaluated on the private dataset and an independent public ultrasound dataset using the Structural Similarity Index (SSIM), Peak Signal-to-Noise Ratio (PSNR), and Visual Information Fidelity (VIF). Comparative experiments with existing restoration methods, ablation analysis, repeated-run evaluation, statistical analysis, and computational analysis were performed. Cross-dataset evaluation assessed generalization without additional fine-tuning. A downstream binary classification experiment using a transfer learning-based ResNet50 classifier was conducted on restored images, with performance assessed using accuracy, precision, sensitivity, specificity, F1-score, and area under the curve (AUC). Results MSERNet reduced JPEG compression artifacts and improved image quality across the evaluated compression levels and datasets, with quantitative metrics supported by visual assessment. Comparative and ablation experiments assessed the method’s performance and component contributions, while repeated-run evaluation examined consistency. Cross-dataset evaluation demonstrated stable restoration performance without additional fine-tuning. The downstream classification experiment indicated that the restored images retained information relevant to cystic-versus-normal classification. Discussion These findings highlight the potential of MSERNet to improve the quality of JPEG-compressed ultrasound images for subsequent analysis. Further validation on larger and more diverse datasets is needed to assess its generalizability and applicability to other medical image-analysis tasks.