Deep Learning-Based Perceptual Stimulus Encoder for Bionic Vision
This work addresses improving artificial vision quality for blind individuals with retinal implants, but it is an incremental step as it builds on existing models and focuses on a specific dataset.
The paper tackles the challenge of generating intelligible visual percepts for retinal implants by proposing a CNN-based perceptual stimulus encoder that predicts electrode activation patterns, achieving effectiveness demonstrated on MNIST with a psychophysically validated model.
Retinal implants have the potential to treat incurable blindness, yet the quality of the artificial vision they produce is still rudimentary. An outstanding challenge is identifying electrode activation patterns that lead to intelligible visual percepts (phosphenes). Here we propose a PSE based on CNN that is trained in an end-to-end fashion to predict the electrode activation patterns required to produce a desired visual percept. We demonstrate the effectiveness of the encoder on MNIST using a psychophysically validated phosphene model tailored to individual retinal implant users. The present work constitutes an essential first step towards improving the quality of the artificial vision provided by retinal implants.