Edge Detection Performance in Super-Resolution Image Reconstruction from Camera Arrays

Previous work has shown that for super-resolution image reconstruction from low resolution images, image acquisition with a diverse optical system improves reconstructed image quality as measured by the expected and actual mean squared error. However, other measures of image fidelity should also be considered. An alternative performance measure might be based on edge errors, since edges are often the first step in more complex image analysis for both image processing systems and biological systems. This paper explores the behavior of edge errors and intensity errors for super-resolution image reconstruction applications in which ill-posed inversions may cause the actual mean squared error to be highly dependent of image content and thus poorly predicted by the expected mean squared error