Robust detection and removal of dust artifacts in retinal images via dictionary learning and sparse-based inpainting

Abstract

Retinal images are acquired with eye fundus cameras which, like any other camera, can suffer from dust particles attached to the sensor and lens. These particles impede light from reaching the sensor, and therefore they appear as dark spots in the image which can be mistaken as small lesions like microaneurysms. We propose a robust method for detecting dust artifacts from more than one image as input and, for the removal, we propose a sparse-based inpainting technique with dictionary learning. The detection is based on a closing operation to remove small dark features. We compute the difference with the original image to highlight the artifacts and perform a filtering approach with a filter bank of artifact models of different sizes. The candidate artifacts are identified via non-maxima suppression. Because the artifacts do not change position in the images, after processing all input images, the candidate artifacts which are not in the same approximate position in different images are rejected and kept unchanged in the image. The experimental results show that our method can successfully detect and remove artifacts, while ensuring the continuity of retinal structures, such as blood vessels. © 2019 SPIE. Downloading of the abstract is permitted for personal use only.