Regularization Method for Solving Denoising and Inpainting Task Using Stacked Sparse Denoising Autoencoders

Abstract

This article offers a regularization method for training stacked sparse denoising autoencoders aimed at designing model description of objects used for image denoising and inpainting. The offered regularization method allows increasing the generalizing ability of model description, which results in greater stability of denoising methods using it with regard to variation of the noise type. This makes the offered method vital for the tasks where noise or image distortion types cannot be known beforehand. Response speed of the offered algorithm enables to use it for dataflow processing. Absence of the need to formalize the physical nature of noises allows applying the approach to processing images received from various sensors, including sensors beyond the visible spectrum, multispectral and other sensors. The article shows the results of applying the offered regularization method in the denoising and inpainting task as exemplified by FERET face image base.