去模糊
图像复原
计算机科学
变压器
人工智能
模式识别(心理学)
计算机视觉
图像(数学)
图像处理
电压
物理
量子力学
作者
Yuqi Jiang,Chune Zhang,Shuo Jin,J. Liu,J. W. Wang
标识
DOI:10.1145/3581783.3612251
摘要
Image restoration is an ill-posed problem due to the infinite feasible solutions for degraded images. Although CNN-based and Transformer-based approaches have been proven effective in image restoration, there are still two challenges in restoring complex degraded images: 1)local-global information extraction and fusion, and 2)computational cost overhead. To address these challenges, in this paper, we propose a lightweight image restoration network (CLG-INet) based on CNN-Transformer interaction, which can efficiently couple the local and global information. Specifically, our model is hierarchically built with a "sandwich-like" structure of coupling blocks, where each block contains three layers in sequence (CNN-Transformer-CNN). The Transformer layer is designed with two core modules: Dynamic Bi-Projected Attention (DBPA), which performs dual projection with large convolutions across windows to capture long-range dependencies, and Gated Non-linear Feed-Forward Network (GNFF), which reconstructs mixed feature information. In addition, we introduce interactive learning, which fuses local features and global representations in different resolutions to the maximum extent. Extensive experiments demonstrate that CLG-INet significantly boosts performance on various image restoration tasks, such as deraining, deblurring, and denoising.
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