Improved Retinex-based low light image enhancement algorithm

Low-light image enhancement is one of the most challenging tasks in computer vision. Traditional unsupervised learning methods typically use an image-to-image transformation model to solve low-light image enhancement problems but are unable to suppress the noise prevalent in images captured in real-world low-light conditions. In order to solve a series of degradation problems such as low brightness, high noise and weak contrast of low light images, this paper proposes a new network architecture to enhance the low light images. The whole network includes three sub networks: decomposition, denoising and enhancement. The decomposition network decomposes the image into illumination image and reflection image. The denoising network denoises the reflection image in the frequency domain. The enhancement network enhances the illumination map through several convolution operations. Finally, the denoised reflection image and the enhanced illumination image are multiplied pixel by pixel to obtain the result image. Experiments show that the proposed method in this paper can effectively improve the brightness and contrast, remove noise and it has obvious advantages in subjective and objective evaluation indexes.