2021/03/04 by Chong Mou, Jian Zhang, Xiaopeng Fan +2 · 94 citations
Computer Science · #Advanced Image Processing Techniques #Artifact (error) #Code (set theory) #Feature (linguistics) #Focus (optics) #Generative Adversarial Networks and Image Synthesis #Image (mathematics) #Image and Signal Denoising Methods #Image compression #Image restoration #Process (computing) #Source code #cs.CV
paper · pdf · doi:10.1109/tmm.2021.3063916
published in IEEE Transactions on Multimedia 24, 1366-1377 (Institute of Electrical and Electronics Engineers) · 11 pages, 6 tables, 9 figures, to be published in IEEE Transactions on Multimedia
openalex publication_date 2021/03/04 · arxiv created 2021/03/10 · arxiv updated 2021/03/11 · openalex created_date 2021/08/02 · openalex updated_date 2026/08/06
Local and non-local attention-based methods have been well studied in various image restoration tasks while leading to promising performance. However, most of the existing methods solely focus on one type of attention mechanism (local or non-local). Furthermore, by exploiting the self-similarity of natural images, existing pixel-wise non-local attention operations tend to give rise to deviations in the process of characterizing long-range dependence due to image degeneration. To overcome these problems, in this paper we propose a novel collaborative attention network (COLA-Net) for image restoration, as the first attempt to combine local and non-local attention mechanisms to restore image content in the areas with complex textures and with highly repetitive details respectively. In addition, an effective and robust patch-wise non-local attention model is developed to capture long-range feature correspondences through 3D patches. Extensive experiments on synthetic image denoising, real image denoising and compression artifact reduction tasks demonstrate that our proposed COLA-Net is able to achieve state-of-the-art performance in both peak signal-to-noise ratio and visual perception, while maintaining an attractive computational complexity. The source code is available onhttps://github.com/MC-E/COLA-Net.