基于局部生成对抗网络的水上低照度图像增强

针对低照度条件下获取的水上图像亮度和对比度低以及质量差的问题，提出一种基于局部生成对抗网络的图像增强方法。以残差网络作为基本框架设计生成器，通过加入金字塔扩张卷积模块提取与学习图像深层特征和多尺度空间特征，从而减少结构信息丢失。设计一个自编码器作为注意力网络，估计图像中的光照分布并指导图像不同亮度区域的自适应增强。构建具有判别图像局部区域能力的判别器结构，约束生成器输出增强效果更加自然的图像。实验结果表明，该方法能够有效增强水上低照度图像，场景还原和细节保留能力优于SRIE和LIME等方法。

Low-Light Maritime Image Enhancement Based on Local Generative Adversarial Network

To address the problems of the maritime images taken in low-light,including low brightness,low contrast and poor quality,this paper proposes an image enhancement method based on a local Generative Adversarial Network(GAN).The generator is designed by taking the residual network as the backbone,and a pyramid dilated convolution module is introduced to extract and learn the deep features and multi-scale spatial features of images,reducing the loss of structure information.At the same time,an autoencoder is designed as an attention network to estimate the light distribution of the image and guide the adaptive enhancement for regions of different brightness.Finally,a discriminator that is able to distinguish local regions of the image is designed to constrain the generator to output images with more natural enhancement effects.Experimental results show that the proposed method can effectively enhance maritime images taken in low-light.Compared with SRIE,LIME and other traditional methods,the proposed method can restore scenes better and retain more details.