Comparative analysis of image processing algorithms for visual prosthesis

Visual prosthesis is one of the emerging technology in biomedical implants which helps to restore useful sight for visually impaired people with retinal degenerative diseases. The visual prosthesis consists of two parts. One is external and the other is internal (surgically implanted). The external part includes a camera, image processor and a transmitter. The image processor captures the video using a camera, encodes the captured video to bit frames that can be recognized by surgically placed retinal stimulator and transmits the coded data through transmitter. The internal implanted part includes a receiver and electrode array. The receiver encodes the received serial bits into electrical signals and stimulates the survival parts of the retina using electrical signals applied through the electrode array. The survival parts then sends a signal to the brain where it is interpreted as image/video. The researchers suggested that an array of 600-1000 electrodes are required to do some basic forms of vision such as reading large letters, recognizing faces. etc. The image processor is the crucial one of visual prosthesis where it captures high resolution image from camera, and resizes it into 1024 resolution image. To extract 1024 pixel vital information, the related image processing algorithms include RGB to Gray conversion, Gamma encoding, Edge Detection and down sampling are described here. The comparative analysis of image processing algorithms provided in this paper will drive an image processing strategy with less computational complexity of achieving better performance that is suitable to implement on MIPS based microcontroller.