Fundus-Based Photoacoustic Vascular Image Denoising and Enhancement

Abstract

Fundus imaging is a great tool for the detection of diabetic retinopathy; however, it often suffers from poor image quality and fails to show the vascular information which is crucial for precise diagnosis. Photoacoustic (PA) imaging is a recently developed non-invasive bioimaging technique that illuminates tissues using nanosecond laser pulses to generate acoustic waves to obtain deep tissue images with optical imaging resolution. In this study, we synthesize PA images from normal and abnormal (glaucoma-affected) retinal fundus images. One of the major limitations of synthetic vascular PA images is noise. To alleviate this problem, we propose to use a dictionary learning-based denoising technique i.e., the K-Singular Value Decomposition (K-SVD). Results are compared with several standard denoising approaches such as the Median filter, Jerman filter, and Frangi filter together with the other learning-based approaches, e.g., orthogonal matching pursuit (OMP), and sequential generalized K-means algorithms (SGK). Our results demonstrate that the K-SVD denoising method exhibits superior performance in denoising glaucoma-affected abnormal retina PA images and normal retina PA images, offering better reconstruction image quality and noise removal. © 2024 SPIE.