Single image super-resolution via phase congruency analysis

Abstract

Single image super-resolution (SR) is a severely unconstrained task. While the self-example-based methods are able to reproduce sharp edges, they perform poorly for textures. For recovering the fine details, higher-level image segmentation and corresponding external texture database are employed in the example-based SR methods, but they involve too much human interaction. In this paper, we discuss the existing problems of example-based technique using scale space analysis. Accordingly, a robust pixel classification method is designed based on the phase congruency model in scale space, which can effectively divide images into edges, textures and flat regions. Then a super-resolution framework is proposed, which can adaptively emphasize the importance of high-frequency residuals in structural examples and scale invariant fractal property in textural regions. Experimental results show that our SR approach is able to present both sharp edges and vivid textures with few artifacts.