Optical coherence tomographic image denoising based on Chi-square similarity and fuzzy logic

Optical coherence tomography (OCT) is a high-resolution optical imaging technology that has been widely used in various fields, such as medical diagnostics, biological systems and materials science. However, because of the low-coherence interference of light, OCT images are inevitably destroyed by speckle noise. To remove speckle noise, an iterative contraction algorithm based on Chi-square similarity and fuzzy logic is proposed in this paper. An OCT image is first divided into a lot of overlapping image blocks, and a Chi-square distance similar block matching is utilized to form a low rank group matrix. Then, the singular value decomposition of the group matrix is performed, and the singular values are contracted by different weights with fuzzy logic. Finally, a pixel intensity fuzzy classification backward projection technique and an adaptive iterative stopping strategy are used to enhance the denoising effect. Extensive experiments are performed on 18 OCT images of the human retina. Compared with several state-of-the-art denoising algorithms, the experimental results show that the proposed algorithm obtains better objective indicators and visual inspection.