Underwater image restoration via background light estimation and depth map optimization

Abstract

In underwater images, the significant sources of distortion are light attenuation and scattering. Existing underwater image restoration technologies cannot deal with the poor contrast and color distortion bias of underwater images. This work provides a new underwater image restoration approach relying on depth map optimization and background light (BL) estimation. First, we build a robust BL estimation model that relies on the prior features of blurriness, smoothness, and the difference between the intensity of the red and blue-green channels. Second, the red-light intensity, difference between light and dark channels, and disparity of red and green-blue channels by considering the hue are used to calculate the depth map. Then, the effect of artificial light sources on the underwater image is removed using the adjusted reversed saturation map. Both the subjective and objective experimental results reveal that the images produced by the proposed technology provide more remarkable visibility and superior color fidelity.