Color image super-resolution reconstruction based on POCS with edge preserving

Abstract

A color image super-resolution (SR) reconstruction based on an improved Projection onto Convex Sets (POCS) in YCbCr space is proposed. Compared with other methods, the POCS method is more intuitive and generally simple to implement. However, conventional POCS algorithm is strict to the accuracy of movement estimation and it is not conducive to the resumption of the edge and details of images. Addressed to these two problems, we on one hand improve the LOG operator to detect edges with the directions of ±0°, ±45°, ±90°, ±135° in order to inhibit the edge degradation. Then, by using the edge information, we proposed a self-adaptive edge-directed interpolation and a modified adaptive direction PSF to construct a reference image as well as to reduce the edge oscillation when revising the reference respectively. On the other hand, instead of block-matching, the Speeded up Robust Feature (SURF) matching algorithm, which can accurately extract the feature points with invariant to affine transform, rotation, scale, illumination changes, are utilized to improve the robustness and real-time in motion estimation. The performance of the proposed approach has been tested on several images and the obtained results demonstrate that it is competitive or rather better in quality and efficiency in comparison with the traditional POCS.