Local self-similarity based image super-resolution reconstruction algorithm
利用图像局部自相似性的超分辨率重构算法

Image super-resolution refers to reconstruction of a high resolution image from one or a set of blurred low resolution images. This paper only pays attention to the kind of reconstruction from one blurred low resolution image. Many methods have been developed for this kind of reconstruction, most of which are MAP methods and interpolation methods. This paper proposed a new interpolation method. The proposed method used the quad tree segmentation to partition the low resolution image, the edge-directed interpolation to each segmented band of the low resolution image, and a wavelet projection to optimize the high resolution image obrained from the local interpolation. The experiment used the peak signal to noise ratio (PSNR) to compare the reconstructed image with the original image. And the results showed that the PSNR and visual effect of the high resolution image reconstructed with the proposed method were very good.