基于Retinex-Net的低照度图像增强算法研究

Research on Low-Light Image Enhancement Algorithm Based on Retinex-Net

视频监控图像在光照条件不足的情况下,会出现图像黑暗、噪声大、颜色失真等问题,严重干扰了人们对于图像内容的判断,使用图像增强算法可以有效改善图像的质量。在对现有流行的图像增强技术研究基础上,提出改进的Retinex-Net算法。该方法将残差网络用于特征的融合,从而得到更加详细的特征图,并通过加入色彩损失解决增强图像出现色彩偏差的问题。实验表明,改进后的Retinex-Net算法提高了图像的亮度和改善图像色彩失真,整体效果有所提升。

In the case of insufficient lighting conditions, video surveillance images will have problems such as dark images, large noise, and color distortion, which seriously interfere with people’s judgment of image content. The use of image enhancement algorithms can effectively improve the quality of images. Based on the research on the existing popular image enhancement techniques, an improved Retinex-Net algorithm is proposed. This method uses the residual network for feature fusion to obtain more detailed feature maps, and solves the problem of color deviation in enhanced images by adding color loss. Experiments show that the improved Retinex-Net algorithm improves the brightness of the image and improves the color distortion of the image, and the overall effect is improved.