结合多分支结构和U-net的低照度图像增强

随着夜景拍摄技术的提高，低照度图像增强成为计算机视觉领域一个新的热点。但是由于光照不足、逆光、聚焦失败等因素的影响会导致光照强度不足，导致图像亮度和对比度过低。为了更好地处理低光照图像，提出了一种基于多分支结构和U-net结合的低照度图像增强算法。利用深度残差网络将图片不同层次的特征提取出来进行交叉合并。将得到的图像通过不同深度和结构的U-net进行增强。将U-net增强后的图像进行融合，最终得到了增强后的低照度图像。通过大量的实验表明，运用深度残差网络和U-net，可以更好地进行特征提取，低照度图像增强的效果也更好，很大程度上优于现有的技术。提出的方法不仅在视觉上提高了亮度和对比度，色彩更真实，更加符合人眼视觉系统特性，而且PSNR、SSIM等七项客观图像质量指标在几种算法中都是最优的。

Low-Light Image Enhancement Using Multi-Branch Structure and U-net

With the improvement of night scene shooting technology, low-light image enhancement has become a new hot spot in the field of computer vision. However, due to the lack of light, backlighting, focusing failure and other factors will lead to insufficient light intensity, resulting in excessively low brightness and contrast of the image. To better process low-light images, a low-light image enhancement algorithm based on multi-branch structure and U-net is proposed. The image features with different levels which are extracted by deep residual network are used for cross-merging. The obtained images are enhanced by U-net with different depths and structures, and then the images enhanced by U-net are fused. The enhanced low illuminance image is obtained. A mass of experiments show that the use of the deep residual network and U-net can better extract features, and the effect of low-intensity image enhancement is better, which is largely superior to the existing technology. The proposed methods not only improve the brightness and contrast visually, the colors are more realistic, and more in line with the characteristics of the human visual system, but also PSNR, SSIM and other seven objective image quality indexes are optimal in several algorithms.