Marine snow detection and removal: underwater image restoration using background modeling

Abstract

It is a common problem that images captured underwater (UW) are corrupted by noise. This is due to the light absorption and scattering by the marine environment; therefore, the visibility distance is limited up to few meters. Despite blur, haze, low contrast, non-uniform lightening and color cast which occasionally are termed noise, additive noises, such as sensor noise, are the center of attention of denoising algorithms. However, visibility of UW scenes is distorted by another source termed marine snow. This signal not only distorts the scene visibility by its presence but also disturbs the performance of advanced image processing algorithms such as segmentation, classification or detection. In this article, we propose a new method that removes marine snow from successive frames of videos recorded UW. This method utilizes the characteristics of such a phenomenon and detects it in each frame. In the meanwhile, using a background modeling algorithm, a reference image is obtained. Employing this image as a training data, we learn some prior information of the scene and finally, using these priors together with an inpainting algorithm, marine snow is eliminated by restoring the scene behind the particles.