Toward developing tangling noise removal and blind inpainting mechanism based on total variation in image processing

Abstract

In the field of image processing, tangling noise and artefacts elimination of objects are two essential tasks. Tangling noise and lack of intensity in certain applications also occur at the same time. In this paper, a new variational model is proposed based on total variation and l(0) the norm for simultaneously removing the tangling noise, estimating the location of missing pixels, and filling in them. To be specific, the total variation is used to regularize the estimated image and use the l(0) norm to make the missing pixel to be sparse. Moreover, the data fidelity term is given by a new forward description about the degraded process and the gamma noise assumption. Finally, an algorithm based on the alternating direction multiplier method is exploited to solve the model. By conducting simulated and real experiments, the damaged images can be effectively restored by the proposed method. In qualitative and quantitative terms, this approach works better.