Research on low-light image enhancement method based on UNet

As technology develops continuously, digital images are being applied more and more extensively in daily life. Digital images are mainly divided into two categories: one is the images obtained by photosensitive devices such as cameras, and the other is the images generated by software. Nevertheless, in low - light conditions, the images captured by traditional photosensitive devices typically suffer from issues like low luminance and subpar visual qualities. These problems pose significant challenges to subsequent image processing and practical applications. Thus, the technology of enhancing low - light images has emerged as a crucial research area for the purpose of elevating image quality. This paper puts forward a solution centered around the improved UNet network to address the issue of image enhancement in low - light settings. The UNet network effectively performs image enhancement through feature extraction, feature fusion, and restoration operations. However, the classical UNet can only process single - channel grayscale images, and the loss function it uses has relatively limited effects in low - light image processing. Therefore, this paper adjusts the number of channels of the UNet network so that it can process three - channel RGB images and improves the loss function. We compared the enhancement effects of L1 Loss, VGG Loss, and SmoothL1 Loss respectively, and carried out a quantitative analysis through evaluation metrics such as PSNR and SSIM. Ultimately, through the combination of the three loss functions and their weighted optimization, the enhancement effect of low - light images is notably enhanced. The innovation of this paper lies in expanding the structure of the UNet network to make it adaptable to three - channel image processing, and improving the quality of image enhancement by improving the loss function. This approach is capable of efficiently enhancing the visual impact of low - light images, thereby offering a practical solution for image processing in real - world applications.