Combined self-learning based single-image super-resolution and dual-tree complex wavelet transform denoising for medical images

In this paper, we propose a novel self-learning based single-image super-resolution (SR) method, which is coupled with dual-tree complex wavelet transform (DTCWT) based denoising to better recover high-resolution (HR) medical images. Unlike previous methods, this self-learning based SR approach enables us to reconstruct HR medical images from a single low-resolution (LR) image without extra training on HR image datasets in advance. The relationships between the given image and its scaled down versions are modeled using support vector regression with sparse coding and dictionary learning, without explicitly assuming reoccurrence or self-similarity across image scales. In addition, we perform DTCWT based denoising to initialize the HR images at each scale instead of simple bicubic interpolation. We evaluate our method on a variety of medical images. Both quantitative and qualitative results show that the proposed approach outperforms bicubic interpolation and state-of-the-art single-image SR methods while effectively removing noise

Lesion Focused Super-Resolution

Super-resolution (SR) for image enhancement has great importance in medical image applications. Broadly speaking, there are two types of SR, one requires multiple low resolution (LR) images from different views of the same object to be reconstructed to the high resolution (HR) output, and the other one relies on the learning from a large amount of training datasets, i.e., LR-HR pairs. In real clinical environment, acquiring images from multi-views is expensive and sometimes infeasible. In this paper, we present a novel Generative Adversarial Networks (GAN) based learning framework to achieve SR from its LR version. By performing simulation based studies on the Multimodal Brain Tumor Segmentation Challenge (BraTS) datasets, we demonstrate the efficacy of our method in application of brain tumor MRI enhancement. Compared to bilinear interpolation and other state-of-the-art SR methods, our model is lesion focused, which is not only resulted in better perceptual image quality without blurring, but also more efficient and directly benefit for the following clinical tasks, e.g., lesion detection and abnormality enhancement. Therefore, we can envisage the application of our SR method to boost image spatial resolution while maintaining crucial diagnostic information for further clinical tasks.Comment: 4 pages, 2 figures, 1 table, Accepted as Oral Presentation by the SPIE Medical Imaging Conference 201