Abstract
licensed under Downloaded from jov.arvojournals.org on 03/19/2022Neural prosthetics may provide a promising solution to restore visual perception in some forms of blindness. The restored prosthetic percept is rudimentary compared to normal vision and can be optimized with a variety of image preprocessing techniques to maximize relevant information transfer. Extracting the most useful features from a visual scene is a nontrivial task and optimal preprocessing choices strongly depend on the context. Despite rapid advancements in deep learning, research currently faces a difficult challenge in finding a general and automated preprocessing strategy that can be tailored to specific tasks or user requirements. In this paper, we present a novel deep learning approach that explicitly addresses this issue by optimizing the entire process of phosphene generation in an end-to-end fashion. The proposed model is based on a deep auto-encoder architecture and includes a highly adjustable simulation module of prosthetic vision. In computational validation experiments, we show that such an approach is able to automatically find a task-specific stimulation protocol. The results of these proof-of-principle experiments illustrate the potential of end-to-end optimization for prosthetic vision.
Original language | English |
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Article number | 20 |
Journal | Journal of vision |
Volume | 22 |
Issue number | 2 |
DOIs | |
Publication status | Published - Feb 2022 |
Keywords
- Computer vision
- Deep learning
- End-to-end optimization
- Prosthetic vision
- UT-Gold-D