Lightweight Zero‐Shot Superresolution Reconstruction of Fundus Images Based on Residual Information Distillation and Multi‐Feature Fusion

Fundus photography provides imaging techniques for the diagnosis of retinal diseases. The diagnostic accuracy, however, heavily relies on the clarity of subtle lesions, which can be significantly affected by image resolution. Achieving a balance between reconstruction quality, model complexity, and training efficiency remains a key challenge, particularly under limited data conditions. To address these issues, a lightweight end‐to‐end model LiteZSSR is proposed for super‐resolution reconstruction of fundus images, incorporating a residual information distillation module to extract multi‐scale features within a shallow network architecture, effectively retaining both local and global contextual information. In addition, a multi‐feature fusion group composed of multiple large kernel attention blocks is designed to strengthen feature representation while minimizing redundancy and computational overhead. Unsupervised training based on internal image learning is adopted to eliminate dependence on large‐scale datasets and to suppress artifacts commonly produced by CNN‐based SRR methods. Extensive experiments on publicly available fundus image datasets, including DRIVE, STARE, and CHASEDB1, demonstrate that LiteZSSR outperforms existing state‐of‐the‐art methods in terms of PSNR and SSIM, while significantly reducing model parameters. These results highlight its potential for practical deployment in clinical fundus image enhancement tasks.