Optimal edge detection for a real-time head mounted display providing low vision aid

Individuals with visual impairments will often suffer from a loss of visual sensitivity to high spatial frequencies that cannot be effectively treated by traditional methods such as optical magnification and contrast enhancement. More recently digital image processing technologies have been applied to aid the visually impaired through augmented vision where the image can be enhanced by various novel techniques such as superimposing high spatial frequencies over the original image. However the computational complexity of digital image processing can severely limit their application to real-time augmented vision. This paper demonstrates that augmented real world environment images with edge detection can provide a significant increase in visually impaired perceived image quality; statistical edge detection was demonstrated to produce the optimum improvement amongst the various edge detectors investigated. The paper then optimises the statistical based edge detection algorithm for suitable deployment on real-time augmented vision embedded platforms.