Single-frame super-resolution by a cortex based mechanism using high level visual features in natural images

Super-resolution is the creation of higher resolution views of pixel-based images through interpolating the original pixels. Natural images are highly redundant on a pixel-by-pixel scale due to local dependencies among pixels, such as lines and textures. Greater super-resolution can be achieved by taking advantage of these local features inherent in natural images. In order to discover and learn these statistically significant features, we used SINBAD method (Set of INteracting BAckpropagating Dendrites), which is a biologically inspired cortical model for perception. SINBAD method allowed us to infer missing pixel values better than standard backprop networks and polynomial interpolation techniques, which are insensitive to the lines or textures in the images. To further test which method preserves edges more accurately, we used Sobel edge detection. SINBAD cells picked local lines and edges as the easiest orderly features in un-preprocessed natural images. Thus, the SINBAD approach appears to follow the same route taken by the brain's processing of visual information.