Phase coherent noise reduction in digital holographic microscopy based on adaptive non-convex sparse regularization

Abstract

Digital holographic microscopy is a new type of quantitative phase measurement tool. When a coherent light source is used for illumination, the phase coherent noise seriously affects the imaging quality and measurement accuracy. A method of phase coherent noise reduction based on adaptive non-convex sparse regularization is proposed in this paper. This denoising problem is considered as a sparse regularization model, which uses a non-convex penalty called the generalized minimax-concave; the penalty can lead to a more accurate estimation of the phase image amplitude and reduce most of the noise. Meanwhile, the threshold of the algorithm to solve this problem is guided by the edge credibility map adaptively. In edge regions, the threshold value is small so that edge details are preserved; however, in other regions, the threshold value is big so that the phase coherent noise is removed. The experimental results confirmed the validity of the proposed method.