一种基于ARD⁃GAN的低照度图像增强方法

Low⁃Light Image Enhancement Method Based on ARD⁃GAN

为解决低照度图像增强过程中噪声放大、细节不足以及色彩还原问题,本文提出一种基于注意力机制残差密集生成对抗网络(Attention Residual Dense⁃Generative Adversarial Networks,ARD⁃GAN)的低照度图像增强方法.首先,该方法在全局光照估计模块(Global Illumination Estimation Module,GIEM)中生成全局曝光注意力图,以引导后续模块更好地进行照度增强;其次,使用卷积残差模块(Convolution and Residual Module,CRM)和基于通道注意力的残差密集模块(Channel Attention Residual Dense Module,CARDM)分别提取浅层特征和深层特征,并将不同层次的特征融合以获取更好的细节信息;然后,在CARDM基础上将密集连接与批归一化相结合抑制噪声;最后改进了损失函数,使增强后图像色彩还原更好.实验表明,ARD⁃GAN有与主流算法相比,在主观视觉和客观评价指标上均得到更好的效果.

In order to solve the problems of noise amplification,insufficient detail and color restoration in the process of low⁃light image enhancement,this paper proposes a method of low⁃light image enhancement based on attention residual dense⁃generative adversarial networks(ARD⁃GAN).Firstly,the method generates a global exposure attention map in the global illumination estimation module(GIEM)to guide the subsequent modules to enhance the illumination better;second⁃ly,it adopts the convolution and residual module(CRM)and the channel attention residual dense module(CARDM)to ex⁃tract shallow features and deep features respectively,and fuses different features of levels to obtain better detailed informa⁃tion;furthermore,based on the CARDM,the dense connection and batch normalization are combined to suppress noise.Fi⁃nally,the improved loss function restores the enhanced image color better.Comprehensive experiments are conducted,which show that ARD⁃GAN can significantly outperform mainstream algorithms in subjective vision and objective evalua⁃tion indicators.