Multi-scale convolution underwater image restoration network

Due to the complex underwater imaging environment and illumination conditions, underwater images have some quality degradation problems, such as low contrast, color distortion, texture blur and uneven illumination, which seriously restrict the application in underwater work. In order to solve these problems, we proposed a multi-scale feature fusion CNN based on underwater imaging model in this paper called Multi-Scale Convolution Underwater Image Restoration Network (MSCUIR-Net). Unlike most previous models that estimated the background light and transmittance, respectively, our model unifies the two parameters into one, predicts the univariate linear physical model through lightweight CNN, and directly generates end-to-end clean images. Based on the underwater imaging model, we synthesized the underwater image training set can simulate the shallow water to deep water environment. Then, we do experiments on synthetic images and real underwater images, and prove the superiority of this method through image evaluation indexes. The experimental results show that MSCUIR-Net has a good effect on underwater image restoration.