关键词: 图像复原, 图像增强, 深度学习, Transformer, 轻量化, 特征融合

Abstract: Recent image restoration and image enhancement methods are difficult to balance the robustness of multiple subtasks with the small number of parameters and computational costs. To solve this problem, this paper proposes a lightweight cross-gating transformer (CGT) for efficient image restoration task. On the one hand, this paper summarizes the limitations of traditional global self-attention mechanism to capture global dependencies, and improves the global self-attention mechanism to a cross-level cross-gating self-attention mechanism. Meanwhile, a lightweight feed-forward neural network is proposed to learn cross-level local dependencies at a very small computational cost and reconstruct clear features in the adjacent locality. On the other hand, in view of the defect that the traditional method of adding or concatenating encoder and decoder equally leads to information interference, a long-distance reset update module is proposed to suppress and update useless information and clear features respectively. This paper conducts extensive quantitative experiments and is compared with 25 state-of-the-art methods on 9 datasets for image denoising, image deraining and low-light image enhancement, respectively. Experimental results prove that the proposed lightweight cross-gating transformer achieves high peak signal-to-noise ratio and structural similarity in image restoration and image enhancement tasks with a small number of parameters and computation, and reconstructs clear images close to real-world scenes, achieving state-of-the-art image restoration performance.

Key words: image restoration, image enhancement, deep learning, Transformer, lightweight, feature fusion