Semantic-Guided Residual Learning for the Quality Assessment of Enhanced Images
Shishun Tian, Zhiwei Lan, Zhengyu Zhang, Ting Su, Xia Li, Lu Zhang · 2025
Image enhancement algorithms are essential for improving visual quality but often introduce new distortions, highlighting the need for reliable image quality assessment (IQA). However, existing IQA methods typically focus on semantic information or distortion-prone regions while ignoring their interactions, resulting in unsatisfactory performance. To address this issue, we propose to integrate semantic information with edge residual learning and design a semantic-guided residual learning IQA framework tailored for enhanced images across diverse scenarios. Specifically, the proposed framework utilizes a covariance-guided encoder to extract semantic information, which is then enhanced using a semantic refinement module. The refined semantic information is subsequently utilized to guide edge residual feature learning in the decoder. Extensive experiments on multiple tasks such as deraining, dehazing, and low-light enhancement demonstrate that our method outperforms state-of-the-art approaches.