Wavelet fusion based image super-resolution restoration of projection onto convex set

This paper proposes a new image super-resolution restoration algorithm. The development of the algorithm is based on the improvement of the classical projection on convex set (POCS) algorithm and wavelet fusion to restore a super-resolution image from a series of low resolution (LR) images. At first, the POCS iteration is used to restore high-resolution (HR) image from every LR image. Then several different rules are chosen to fuse HR images in wavelet domain, and a HR image is reconstructed by inverse wavelet transform. The reconstructed image is evaluated by entropy, cross entropy, definition and the peak signal-noise ratio. The experimental results of the processed CT images showed that this method can improve the ability of fusing different image information, and the texture of the image is more prominent, the image quality is higher.