Low-light Image Enhancement via Multispectral Reconstruction

Low-light images often fail to accurately capture color and texture, limiting their practical applications in imaging technology. The use of low-light image enhancement technology can effectively restore the color and texture information contained in the image. However, current low-light image enhancement is directly calculated from low-light to normal-light images, ignoring the basic principles of imaging, and the image enhancement effect is limited. The Retinex model splits the image into illumination components and reflection components, and uses the decomposed illumination and reflection components to achieve end-to-end enhancement of low-light images. Inspired by the Retinex theory, this study proposes a low-light image enhancement method based on multispectral reconstruction. This method first uses a multispectral reconstruction algorithm to reconstruct a metameric multispectral image of a normal-light RGB image. Then it uses a deep learning network to learn the end-to-end mapping relationship from a low-light RGB image to a normal-light multispectral image. In this way, any low-light image can be reconstructed into a normal-light multispectral image. Finally, the corresponding normal-light RGB image is calculated according to the colorimetry theory. To test the proposed method, the popular dataset for low-light image enhancement, LOw-Light (LOL) is adopted to compare the proposed method and the existing methods. During the test, a multispectral reconstruction method based on reversing the image signal processing of RGB imaging is used to reconstruct the corresponding metameric multispectral image of each normal-light RGB image in LOL. The deep learning architecture proposed by Zhang et al. with the convolutional block attention module added is used to establish the mapping relationship between the low-light RGB images and the corresponding reconstructed multispectral images. The proposed method is compared to existing methods such as self-supervised, RetinexNet, RRM, KinD, RUAS, and URetinex-Net. In the context of the LOL dataset and an illuminant chosen for rendering, the results show that the low-light image enhancement method proposed in this study is better than the existing methods.