摘要
针对现存的低照度图像视觉效果差和图像质量低的问题,提出了一种基于级联残差生成对抗网络的低照度图像增强算法,该算法将构建的级联残差卷积神经网络作为生成器网络和改进的PatchGAN作为判别器网络。首先根据Retinex理论,通过正常照度图像合成训练样本,再将低照度图像从RGB空间转换到HSV颜色空间,保持色调分量和饱和度分量不变,利用级联残差生成器网络对亮度分量增强。通过判别器网络监督生成器网络不断增强低照度图像,二者相互博弈,最终使生成器网络具备较好的低照度图像增强的能力。实验结果表明,本文增强算法在合成的低照度图像和自然的低照度图像上,获得了更为良好的视觉效果和对比度,特别在合成的低照度图像上,其峰值信噪比和结构相似度明显优于其他对比算法。
ing at the problem of poor visual effect and low image quality of existing low-light images,a low-light image enhancement algorithm based on cascaded residual generative adversarial network is proposed.The algorithm uses constructed cascaded residual convolutional neural network as generator network and improved PatchGAN as discriminator network.First,training samples are synthesized through normal-light image on the basis of Retinex theory.Then,low-light images are converted from red-green-blue space to hue-saturation-value color space.Meanwhile,keeping hue and saturation unchanged,the value component is enhanced through the cascaded residual generator network.Besides,low-light image is enhanced through the way of discriminator network supervising generator network.They struggle against each other to promote the capability of generator network to enhance the low-light image.Experimental results show that the proposed enhancement algorithm obtains better visual effects and contrast in terms of synthetic low-light images and natural low-light images.Especially,for the synthetic low-light images,the proposed algorithm is obviously superior to other comparison algorithms in terms of peak signal-to-noise ratio and structural similarity.
作者
陈清江
屈梅
Chen Qingjiang;Qu Mei(School of Science,Xi'an University of Architecture and Technology,Xi'an,Shaanxi 710055,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2020年第14期215-224,共10页
Laser & Optoelectronics Progress
基金
国家自然科学基金(61403298)
陕西省自然科学基金(2015JM1024)。
作者简介
屈梅,E-mail:862907196@qq.com。
