Designing and testing scene enhancement algorithms for patients with retina degenerative disorders.

BACKGROUND: Retina degenerative disorders represent the primary cause of blindness in UK and in the developed world. In particular, Age Related Macular Degeneration (AMD) and Retina Pigmentosa (RP) diseases are of interest to this study. We have therefore created new image processing algorithms for enhancing the visual scenes for them. METHODS: In this paper we present three novel image enhancement techniques aimed at enhancing the remaining visual information for patients suffering from retina dystrophies. Currently, the only effective way to test novel technology for visual enhancement is to undergo testing on large numbers of patients. To test our techniques, we have therefore built a retinal image processing model and compared the results to data from patient testing. In particular we focus on the ability of our image processing techniques to achieve improved face detection and enhanced edge perception. RESULTS: Results from our model are compared to actual data obtained from testing the performance of these algorithms on 27 patients with an average visual acuity of 0.63 and an average contrast sensitivity of 1.22. Results show that Tinted Reduced Outlined Nature (TRON) and Edge Overlaying algorithms are most beneficial for dynamic scenes such as motion detection. Image Cartoonization was most beneficial for spatial feature detection such as face detection. Patient's stated that they would most like to see Cartoonized images for use in daily life. CONCLUSIONS: Results obtained from our retinal model and from patients show that there is potential for these image processing techniques to improve visual function amongst the visually impaired community. In addition our methodology using face detection and efficiency of perceived edges in determining potential benefit derived from different image enhancement algorithms could also prove to be useful in quantitatively assessing algorithms in future studies

Modeling a Low Vision Observer: Application in Comparison of Image Enhancement Methods

Issu de : 22nd International Conference, HCII 2020, Copenhagen, Denmark, July 19–24, 2020, Proceedings, Part IIInternational audienceNumerous image processing methods have been proposed to help low vision people, often relied on contrast enhancement algorithms. Their assessment is usually performed by tests on low vision subjects, which are expensive and time consuming. This paper presents a low vision observer model, fully customizable to fit various impaired visual performances, which may be used for early algorithm assessment, and avoiding unnecessary human tests. This model is fitted to visual performances of a subject with degenerative retinal disease, and applied to images processed by two edge enhancement algorithms, allowing to explain their performances in terms of blur reduction and color saturation improvement