Age-related macular degeneration is the leading cause of vision loss among Americans aged >65 years. Currently, no effective treatment can reverse the central vision loss associated with most age-related macular degeneration. Digital image-processing techniques have been developed to improve image visibility for peripheral vision; however, both the selection and efficacy of such methods are limited. Progress has been difficult for two reasons: the exact nature of image enhancement that might benefit peripheral vision is not well understood, and efficient methods for testing such techniques have been elusive. The current study aims to develop both an effective image enhancement technique for peripheral vision and an efficient means for validating the technique.
We used a novel contour-detection algorithm to locate shape-defining edges in images based on natural-image statistics. We then enhanced the scene by locally boosting the luminance contrast along such contours. Using a gaze-contingent display, we simulated central visual field loss in normally sighted young (aged 18–30 years) and older adults (aged 58–88 years). Visual search performance was measured as a function of contour enhancement strength [“Original” (unenhanced), “Medium,” and “High”]. For preference task, a separate group of subjects judged which image in a pair “would lead to better search performance.”
We found that although contour enhancement had no significant effect on search time and accuracy in young adults, Medium enhancement resulted in significantly shorter search time in older adults (about 13% reduction relative to Original). Both age-groups preferred images with Medium enhancement over Original (2–7 times). Furthermore, across age-groups, image content types, and enhancement strengths, there was a robust correlation between preference and performance.
Our findings demonstrate a beneficial role of contour enhancement in peripheral vision for older adults. Our findings further suggest that task-specific preference judgments can be an efficient surrogate for performance testing.