Oct Image Blind Despeckling Based on Gradient Guided Filter with Speckle Statistical Prior
Sanqian Li, Muxing Xiong, Bing Yang, Xiaoqing Zhang, Risa Higashita, Jiang Liu
Abstract
Optical coherence tomography (OCT) imaging technique has been widely used for ocular disease diagnosis. However, speckles occur in OCT images due to the property of coherent imaging, inevitably affecting the visual quality and clinical analysis. To alleviate this problem, we propose a novel gradient-guided speckle image filtering method (GGSF) with structure enhancement for directly removing speckles in OCT images. Specifically, the multiplicative characteristic of speckle noise is incorporated into the guided filtering processing for modeling raw OCT images. To avoid getting trapped in image distortions, we further employ gradient regularization to integrate the structure prior information into the guided speckle image filtering procedure. Additionally, we introduce the statistical property of speckle noise obeying a gamma distribution into the least square method solver for the resulting non-convex GGSF model. Experimental results on the AS-OCT dataset demonstrate the effectiveness of GGSF for OCT image despeckling compared with competitive methods. Furthermore, we validate the benefits of GGSF for subsequent clinical analysis with the CM-OCT dataset.
BibTeX
@inproceedings{icassp2023_octimageblinddes,
title = {Oct Image Blind Despeckling Based on Gradient Guided Filter with Speckle Statistical Prior},
author = {Sanqian Li and Muxing Xiong and Bing Yang and Xiaoqing Zhang and Risa Higashita and Jiang Liu},
booktitle = {ICASSP 2023},
year = {2023}
}