Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship aggregation methods have been widely used in computer vision, but these methods are difficult to handle high-resolution images because of the high computational complexity. To solve this problem, this paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. Based on LASA, Linear Array Network (LAN) is proposed, which is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters. The code is released in https://github.com/cuiziteng/LASA_enhancement.
翻译:以电流神经网络为基础的方法由于其出色的性能而主导了低光图像增强任务。然而,电流操作以本地滑动窗口机制为基础,难以构建地貌图的长距离依赖性。与此同时,基于自我注意的全球关系汇总方法在计算机视觉中被广泛使用,但由于计算复杂程度高,这些方法难以处理高分辨率图像。为了解决这一问题,本文件提议了一个线形阵列自控(LASA)机制,它只使用两个二维特性编码来构建三维全球重量,然后改进同源层生成的地貌图。基于LASA, 提出了线形阵列网络(局),它优于RGB和RAW的低光度增强任务中现有的状态(SOTA)方法,其参数较少。代码发布于 https://github.com/cuiziteng/LASA_enhancement。