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September 10, 2025IET Image Processing0 citationsOpen Access

Lightweight Model for Superresolution of Fundus Images Using Residual Distillation

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

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Authors

XGXiaoxin GuoGYGuangqi YangWWWeijia Wu

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Overview

Proposed model shows improved PSNR and SSIM in fundus images, highlighting efficiency with limited data.

Key Points

  • LiteZSSR achieves superior image quality in fundus images, enhancing diagnostic potential for retinal diseases.
  • It incorporates a residual information distillation module to extract multi-scale features effectively.
  • Unsupervised training reduces reliance on large datasets while minimizing artifacts from conventional methods.
  • Extensive experiments demonstrate improved performance compared to existing techniques with fewer model parameters.

Cite This Study

Guo et al. (2025) studied this question.

synapsesocial.com/papers/68c1c23d54b1d3bfb60efe27https://doi.org/10.1049/ipr2.70178
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1The Use of an Improved Lightweight Scalable Attention-Guided Super-Resolution Method for Remote Sensing Image Enhancement2026
  2. 2Infrared Image Super-Resolution via Lightweight Information Split Network2024
  3. 3Super-resolution reconstruction of images based on residual dual-path interactive fusion combined with attention2024
  4. 4OSFFNet: Omni-Stage Feature Fusion Network for Lightweight Image Super-Resolution2024 · 22 citations
  5. 5Zero-Shot Image Super-Resolution Using Prompt-Driven Vision-Language Foundation Models Without Task-Specific Fine-Tuning2025