A real-time image optimization strategy based on global saliency detection for artificial retinal prostheses

Abstract

Current retinal prostheses can only generate low-resolution visual percepts constituted of inadequate phosphenes which are elicited by a limited number of stimulating electrodes and with unruly color and restricted grayscale. Fortunately, for most retinal prostheses, an external camera and a video processing unit are employed to be essential components, and allow image processing to improve visual perception for recipients. At present, there have been some studies that use a variety of sophisticated image processing algorithms to improve prosthetic vision perception. However, most of them cannot achieve real-time processing due to the complexity of the algorithms and the limitation of platform processing power. This greatly curbs the practical application of these algorithms on the retinal prostheses. In this study, we propose a real-time image processing strategy based on a novel bottom-up saliency detection algorithm, aiming to detect and enhance foreground objects in a scene. Results demonstrate by verification of conducting two eye-hand-coordination visual tasks that under simulated prosthetic vision, our proposed strategy has noticeable advantages in terms of accuracy, efficiency, and head motion range. The study aims to help develop image processing modules in retinal prostheses, and is hoped to provide more benefit towards prosthesis recipients.