DenseNet生成对抗网络低照度图像增强方法

针对低照度环境下采集图像存在低信噪比、低分辨率和低照度的问题,提出了一种基于稠密连接网络(DenseNet)生成对抗网络的低照度图像增强方法.利用DenseNet框架建立生成器网络,并将PatchGAN作为判别器网络;将低照度图像传入生成器网络生成照度增强图像,同时利用判别器网络负责监督生成器对低照度图像的增强效果,通...

Low Illumination Image Enhancement Method Based on DenseNet GAN

Aiming at the problems of low SNR, low resolution and low illumination in low illumination environment, a low illumination image enhancement method based on DenseNet generation countermeasure network is proposed. Firstly, the DenseNet framework is used to establish the generator network, and PatchGAN is used as the discriminator network. Secondly, the low illumination image is transferred into the generator network to generate the illumination enhanced image, and the discriminator network is used to supervise the enhancement effect of the generator on the low illumination image. Through the game between the generator and the discriminator, the network weight is continuously optimized, and finally the generator has good enhancement effect on low illumination image. Experimental results show that, compared with the existing mainstream methods, this method not only has obvious advantages in brightness enhancement and clarity restoration of low illumination images, but also has significant advantages in objective evaluation indexes of image quality such as peak signal-to-noise ratio and structure similarity.