Underwater image colour constancy based on DSNMF

Abstract

Different wavelengths of light may undergo changes in underwater environment resulting in altered images. For example, the presence of floating particles causes underwater images to appear bluish and blurred. In this study, the authors propose a method called the deep sparse non-negative matrix factorisation (DSNMF) to estimate the illumination of an underwater image. The image under observation is divided into patches and each channel of a single patch is reshaped as an [ R, G, B ] matrix. The DSNMF method deeply factorises each input matrix into multiple layers with a sparseness constraint. The last layer of the factorised matrix is used as the illumination of the patch. The sparseness constraint adjusts the appearance of the final image. After factorisation, the estimated illumination is applied to each patch of the original image to obtain the final image. Compared with state-of-the-art underwater image enhancement methods using no reference image quality assessment, not only does the proposed method outperforms current techniques in terms of its visual effect and IQA, but is also simpler to implement.