摘要
针对Retinex-Net存在噪声较大、颜色失真的问题,基于Retinex-Net的分解-增强架构,文中提出改进Retinex-Net的低光照图像增强算法.首先,设计由浅层上下采样结构组成的分解网络,将输入图像分解为反射分量与光照分量,在此过程加入去噪损失,抑制分解过程产生的噪声.然后,在增强网络中引入注意力机制模块和颜色损失,旨在增强光照分量亮度的同时减少颜色失真.最后,反射分量和增强后的光照分量融合成正常光照图像输出.实验表明,文中算法在有效提升图像亮度的同时降低增强图像噪声.
Aiming at the problems of high noise and color distortion in Retinex-Net algorithm,a low-light image enhancement algorithm based on improved Retinex-Net is proposed grounded on the decomposition-enhancement framework of Retinex-Net.Firstly,a decomposition network composed of shallow upper and lower sampling structure is designed to decompose the input image into reflection component and illumination component.In this process,the denoising loss is added to suppress the noise generated during the decomposition process.Secondly,the attention mechanism module and color loss are introduced into the enhancement network to enhance the brightness of the illumination component and meanwhile reduce the image color distortion.Finally,the reflection component and the enhanced illumination component are fused into the normal illumination image to output.The experimental results show that the proposed algorithm improves the image brightness effectively with the noise of enhanced image reduced.
作者
欧嘉敏
胡晓
杨佳信
OU Jiamin;HU Xiao;YANG Jiaxin(School of Electronics and Communication Engineering,Guang-zhou University,Guangzhou 510006)
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2021年第1期77-86,共10页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金项目(No.62076075)资助。
