Super resolution reconstruction of medical image based on adaptive quad-tree decomposition

Medical CT imaging has an important sense in the treatment process. However, the low resolution of CT images could easily affect the final diagnosis, which is influenced by the resolution and the radiation dosage. We propose to solve this problem by using an adaptive image super-resolution reconstruction algorithm. First, the algorithm of the CT image of quad-tree decomposition obtains adaptive access to different scales of the image patches. Then, we exploit K-means clustering algorithm to determine the cluster center. Using the center of cluster, we can obtain the mapping function between the low-resolution image patches and the high-resolution image patches. Finally, the algorithm reconstructs a high-resolution image through the mapping function. The experimental results have shown that the proposed method is capable of enhanced CT image reconstruction, peak signal-to-noise ratio (PSNR) and structural similarity (SSIM).