Image enhancement can accentuate image feature and is necessary process in image processing. This work focuses on fusing multi-exposure image sequences low-light image enhancement. Inspired by the classical non-local means in computer vision, we proposed an improved deep neural network framework with attentions for image enhancement. Firstly, the original image was preprocessed in different dimensions. we get the edge images using an edge extracted algorithm and fusion multi exposed images to get an better initial images based on fully convolutional neural network with position and channel attention mechanism. Secondly, the head network is constructed by fully convolutional neural network. For capturing long-range dependencies between features maps, we designed a non-local attention module for head network to get better enhancement image. Finally, emerging the original images, edge image and fusion image as the input of the head network, it can enhance the images to get high-quality images. Experiments show that our framework proposed in this paper is effective and the attention mechanism play a significant hole in the network.

图像增强可以突出图像特征，是图像处理中必不可少的过程。这项工作的重点是融合多曝光图像序列低光图像增强。受计算机视觉中经典的非局部方法的启发，我们提出了一种改进的深度神经网络框架，关注图像增强。首先，对原始图像进行不同维度的预处理。我们使用边缘提取算法和融合多曝光图像获得边缘图像，以基于具有位置和通道注意机制的全卷积神经网络获得更好的初始图像。其次，头部网络由全卷积神经网络构建。为了捕获特征图之间的长程依赖关系，我们为头部网络设计了一个非局部注意模块以获得更好的增强图像。最后，将原始图像、边缘图像和融合图像作为头部网络的输入，可以对图像进行增强以获得高质量的图像。实验表明，我们在本文中提出的框架是有效的，并且注意力机制在网络中发挥了重要作用。