2024/03/15 by Yixiao Li, Li, Yixiao, Xiaoyuan Yang +7 · 2 citations
Computer Science · Engineering · #Advanced Image Fusion Techniques #Advanced Image Processing Techniques #FOS: Computer and information sciences #Image Processing Techniques and Applications #Multimedia (cs.MM)
paper · pdf · doi:10.48550/arxiv.2403.10406
openalex publication_date 2024/03/15 · openalex created_date 2024/03/19 · openalex updated_date 2026/07/28
There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially dual-branch algorithms, to automatically evaluate the visual quality of SR images remains challenging. Existing SR image quality assessment (IQA) metrics based on two-stream networks lack interactions between branches. To address this, we propose a novel full-reference IQA (FR-IQA) method for SR images. Specifically, producing SR images and evaluating how close the SR images are to the corresponding HR references are separate processes. Based on this consideration, we construct a deep Bi-directional Attention Network (BiAtten-Net) that dynamically deepens visual attention to distortions in both processes, which aligns well with the human visual system (HVS). Experiments on public SR quality databases demonstrate the superiority of our proposed BiAtten-Net over state-of-the-art quality assessment methods. In addition, the visualization results and ablation study show the effectiveness of bi-directional attention.