Image enhancement has the potential to provide elderly macular disease and cataract patients a better quality of life through the use and enjoyment of printed photos and television. Image enhancement is also a necessary component in the design of a new generation of electronic mobility aids. This study will first identify the best algorithm for the enhancement of still images and then formulate a real-time (video rate) version of the algorithm that can be implemented with current technology. Finally the effects of temporal factors such as image motion and eye movement will be evaluated;temporal processing will compensate for these effects. Improvement in recognition attained with various enhancement techniques will be evaluated for patients with central visual field loss or optical media opacities. Performance with filters designed to compensate for the patient's specific visual loss based on measured contrast sensitivity function will be compared with the improvement obtained with general heuristic enhancement techniques. Methods to improve the utilization of the limited dynamic range of the displays will be evaluated. Models for contrast perception and the perception of filtered images will be further developed and evaluated in the context of image enhancement. Patient's contrast sensitivity function will be measured to determine specific loss. Enhancement will compensate for loss in all spatial frequencies in one experiment and only for the highest visible frequency band in another. to utilize the limited range of luminances available on the display, partial compensation and saturation of the highest visible band will be used and evaluated. Patient recognition performance with the different enhancements will be evaluated with a criterion-free estimation methods using correlated receiver operating curve (ROC) analysis. The familiarity of the celebrities whose photos are presented to the patients will be verified with the patient's better eye after the experiment/ Patient's eye movement scanpath while examining faces will be compared with normal observes' scanpath using the scanning laser ophthalmoscope. The effect os image motion on enhanced images will be determined both by using full-face simple image motion and by measurement of patient's ability to correctly recognize changes in facial expression.
| Vera-Diaz, Fuensanta A; Woods, Russell L; Peli, Eli (2017) Blur Adaptation to Central Retinal Disease. Invest Ophthalmol Vis Sci 58:3646-3655 |
| Jung, Jae-Hyun; Pu, Tian; Peli, Eli (2016) Comparing object recognition from binary and bipolar edge images for visual prostheses. J Electron Imaging 25: |
| Jung, Jae-Hyun; Pu, Tian; Peli, Eli (2016) Comparing object recognition from binary and bipolar edge features. IS&T Int Symp Electron Imaging 2016: |
| García-Pérez, Miguel A; Peli, Eli (2015) Aniseikonia Tests: The Role of Viewing Mode, Response Bias, and Size-Color Illusions. Transl Vis Sci Technol 4:9 |
| Jung, Jae-Hyun; Aloni, Doron; Yitzhaky, Yitzhak et al. (2015) Active confocal imaging for visual prostheses. Vision Res 111:182-96 |
| Radhakrishnan, Aiswaryah; Sawides, Lucie; Dorronsoro, Carlos et al. (2015) Single neural code for blur in subjects with different interocular optical blur orientation. J Vis 15:15 |
| Haun, Andrew M; Peli, Eli (2015) Similar Sensitivity to Ladder Contours in Macular Degeneration Patients and Controls. PLoS One 10:e0128119 |
| Hwang, Alex D; Peli, Eli (2014) An augmented-reality edge enhancement application for Google Glass. Optom Vis Sci 91:1021-30 |
| Haun, Andrew M; Peli, Eli (2014) Binocular rivalry with peripheral prisms used for hemianopia rehabilitation. Ophthalmic Physiol Opt 34:573-9 |
| Dilks, Daniel D; Julian, Joshua B; Peli, Eli et al. (2014) Reorganization of visual processing in age-related macular degeneration depends on foveal loss. Optom Vis Sci 91:e199-206 |
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