注意力机制下的多阶段低照度图像增强网络

对于低照度图像增强过程中，因图像内容重叠且部分区域亮度差异较大导致的图像细节丢失的问题，提出一个注意力机制下的多阶段低照度图像增强网络。第一阶段利用改进的多尺度融合模块对图像进行初步增强；第二阶段利用第一阶段增强后的图像信息与本阶段的输入进行级联，并将其结果作为该阶段多尺度融合模块的输入；第三阶段利用第二阶段增强后的图像信息与该阶段的输入级联，并将其结果作为该阶段多尺度融合模块的输入。这样利用多阶段的方式完成自适应的亮度提升和细节的保留。在公开数据集LOL和SICE上的实验结果表明，相较于MSR算法、灰度直方图均衡化（HE）算法和RetinexNet等算法和网络，所提网络的峰值信噪比（PSNR）的数值提高了11.0%～28.9%，结构相似性（SSIM）的数值提高了6.8%～46.5%。所提网络利用多阶段和注意力机制实现低照度图像增强，有效解决了图像内容重叠和亮度差异大的问题，得到的图像细节更丰富，纹理更清晰，主观辨识度更高。

Multi-stage low-illuminance image enhancement network based on attention mechanism

A multi-stage low-illuminance image enhancement network based on attention mechanism was proposed to solve the problem that the details of low-illuminance images are lost due to the overlapping of image contents and large brightness differences in some regions during the enhancement process of low-illuminance images. At the first stage， an improved multi-scale fusion module was used to perform preliminary image enhancement. At the second stage， the enhanced image information of the first stage was cascaded with the input of this stage， and the result was used as the input of the multi-scale fusion module in this stage. At the third stage， the enhanced image information of the second stage was cascaded with the input of the this stage， and the result was used as the input of the multi-scale fusion module in this stage. In this way， with the use of multi-stage fusion， not only the brightness of the image was improved adaptively， but also the details were retained adaptively. Experimental results on open datasets LOL and SICE show that compared to the algorithms and networks such as MSR （Multi-Scale Retinex） algorithm， gray Histogram Equalization （HE） algorithm and RetinexNet （Retina cortex Network）， the proposed network has the value of Peak Signal-to-Noise Ratio （PSNR） 11.0% to 28.9% higher， and the value of Structural SIMilarity （SSIM） increased by 6.8% to 46.5%. By using multi-stage method and attention mechanism to realize low-illuminance image enhancement， the proposed network effectively solves the problems of image content overlapping and large brightness difference， and the images obtained by this network are more detailed and subjective recognizable with clearer textures.