A STUDY ON LIGHT FIELD DENOISING FOR 3D CONSISTENT VISUALIZATION

We propose a novel method of light field denoising for 3D consistent visualization. Light field data are often significantly degraded by awful noise due to lack of exposure under insufficient lighting, especially, if acquired by a lens array. Then, recent studies notice that a 4D light field corresponds to structured multi-view images that are correlated to each other well according to 3D scenes. For example, a CNN-based denoising method utilizes the correlation by analyzing various subspaces of 4D light fields. In this paper, 4D light field denoising is achieved by applying simple restoration based on Total Variation minimization to small images composed of corresponding pixels extracted from multi-view images. We show experimental results using synthetic and real images to clarify how our proposed method effectively works for 3D consistent visualization in comparison with the other methods. In addition, we discuss possible future improvement of the proposed method when integrating our novel approach with the conventional 2D image denoising for more effective light field denoising.