Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
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2025
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| author | Wu, Haofan Huang, Yin Wu, Yuqing Yang, Qiuyu Wang, Bingfang Zhang, Li Khan, Muhammad Fahadullah Zia, Ali Memon, M. Saleh Bukhari, Syed Sohail Memon, Abdul Fattah Ji, Daizong Zhang, Ya Mustafa, Ghulam Fang, Yin |
| author_facet | Wu, Haofan Huang, Yin Wu, Yuqing Yang, Qiuyu Wang, Bingfang Zhang, Li Khan, Muhammad Fahadullah Zia, Ali Memon, M. Saleh Bukhari, Syed Sohail Memon, Abdul Fattah Ji, Daizong Zhang, Ya Mustafa, Ghulam Fang, Yin |
| contents | High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variability, and patient compliance, fundus images often suffer from low resolution and signal-to-noise ratio. Recent years have witnessed promising progress in fundus image enhancement. However, existing works usually focus on restoring structural details or global characteristics of fundus images, lacking a unified image enhancement framework to recover comprehensive multi-scale information. Moreover, few methods pinpoint the target of image enhancement, e.g., lesions, which is crucial for medical image-based diagnosis. To address these challenges, we propose a multi-scale target-aware representation learning framework (MTRL-FIE) for efficient fundus image enhancement. Specifically, we propose a multi-scale feature encoder (MFE) that employs wavelet decomposition to embed both low-frequency structural information and high-frequency details. Next, we design a structure-preserving hierarchical decoder (SHD) to fuse multi-scale feature embeddings for real fundus image restoration. SHD integrates hierarchical fusion and group attention mechanisms to achieve adaptive feature fusion while retaining local structural smoothness. Meanwhile, a target-aware feature aggregation (TFA) module is used to enhance pathological regions and reduce artifacts. Experimental results on multiple fundus image datasets demonstrate the effectiveness and generalizability of MTRL-FIE for fundus image enhancement. Compared to state-of-the-art methods, MTRL-FIE achieves superior enhancement performance with a more lightweight architecture. Furthermore, our approach generalizes to other ophthalmic image processing tasks without supervised fine-tuning, highlighting its potential for clinical applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_01831 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement Wu, Haofan Huang, Yin Wu, Yuqing Yang, Qiuyu Wang, Bingfang Zhang, Li Khan, Muhammad Fahadullah Zia, Ali Memon, M. Saleh Bukhari, Syed Sohail Memon, Abdul Fattah Ji, Daizong Zhang, Ya Mustafa, Ghulam Fang, Yin Image and Video Processing Computer Vision and Pattern Recognition High-quality fundus images provide essential anatomical information for clinical screening and ophthalmic disease diagnosis. Yet, due to hardware limitations, operational variability, and patient compliance, fundus images often suffer from low resolution and signal-to-noise ratio. Recent years have witnessed promising progress in fundus image enhancement. However, existing works usually focus on restoring structural details or global characteristics of fundus images, lacking a unified image enhancement framework to recover comprehensive multi-scale information. Moreover, few methods pinpoint the target of image enhancement, e.g., lesions, which is crucial for medical image-based diagnosis. To address these challenges, we propose a multi-scale target-aware representation learning framework (MTRL-FIE) for efficient fundus image enhancement. Specifically, we propose a multi-scale feature encoder (MFE) that employs wavelet decomposition to embed both low-frequency structural information and high-frequency details. Next, we design a structure-preserving hierarchical decoder (SHD) to fuse multi-scale feature embeddings for real fundus image restoration. SHD integrates hierarchical fusion and group attention mechanisms to achieve adaptive feature fusion while retaining local structural smoothness. Meanwhile, a target-aware feature aggregation (TFA) module is used to enhance pathological regions and reduce artifacts. Experimental results on multiple fundus image datasets demonstrate the effectiveness and generalizability of MTRL-FIE for fundus image enhancement. Compared to state-of-the-art methods, MTRL-FIE achieves superior enhancement performance with a more lightweight architecture. Furthermore, our approach generalizes to other ophthalmic image processing tasks without supervised fine-tuning, highlighting its potential for clinical applications. |
| title | Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2505.01831 |