Medical image super-resolution by using multi-dictionary and random forest. (February 2018) - Wei, Shuaifang
Highlights: Patches are clustered iteratively for least super-resolving error. Dictionaries are learned for each cluster. Random forest is trained offline to select most suitable dictionary for each patch. Abstract: Smart City has become the direction of the development of city. Telemedicine is an important part of Smart City. Telemedicine always provides clinical health care according to the medical images of the patient. High resolution images are expected for remote diagnosis. Super-resolution technology can improve the resolution of medical images. Recently, sparse coding based super-resolution has attracted more attentions. Sparse coding based super-resolution tries to find the sparse representation of low-resolution (LR) image patches from low resolution dictionary, then reconstructs high-resolution (HR) image patches using sparse representation and HR dictionary. In this paper, we propose a sparse-based scheme for medical image super-resolution. First, we jointly divide the training patches into several clusters. Multiple dictionaries are learned from each cluster to collectively provide the least super-resolution error for the training patches. Second, random forest is trained based on the training patches and their cluster labels. Finally, for an input LR image patches, we use trained random forest to determine which cluster the patch belong to, then use the corresponding dictionary to reconstruct the patch. Thus, all the input LR patches are reconstructed with smallest error. All the reconstructed HR patches are synthesized into a completed HR image. The proposed scheme is applied to test a set of medical images. Experimental results show that both objective evaluation (PSNR) and subjective evaluation (visual effect) are improved when compare to other example-based methods.