Example-based super-resolution for enhancing spatial resolution of medical images.

This paper proposes an effective exampled-based super-resolution (SR) method to improve the spatial resolution of medical image heavily corrupted by noise. Based on the sparsity of patches, the reconstruction of a high-resolution (HR) patch from each low-resolution (LR) input patch can be performed with the help of a database, by solving a non-negative sparse optimization problem. The challenge is to effectively solve this problem in case of a large size database. To cope with this issue, we propose a metric to measure the similarity between image patches based on the Earth Mover's Distance (EMD) in order to select only the most similar candidates that will be used in the optimization problem. Experimental results demonstrate the efficiency of our algorithm over many existing SR methods, especially in case of noise-corrupted LR image.

Novel example-based method for super-resolution and denoising of medical images.

In this paper, we propose a novel example-based method for denoising and super-resolution of medical images. The objective is to estimate a high-resolution image from a single noisy low-resolution image, with the help of a given database of high and low-resolution image patch pairs. Denoising and super-resolution in this paper is performed on each image patch. For each given input low-resolution patch, its high-resolution version is estimated based on finding a nonnegative sparse linear representation of the input patch over the low-resolution patches from the database, where the coefficients of the representation strongly depend on the similarity between the input patch and the sample patches in the database. The problem of finding the nonnegative sparse linear representation is modeled as a nonnegative quadratic programming problem. The proposed method is especially useful for the case of noise-corrupted and low-resolution image. Experimental results show that the proposed method outperforms other state-of-the-art super-resolution methods while effectively removing noise.

Enhanced SWIFT acquisition with chaotic compressed sensing by designing the measurement matrix with hyperbolic-secant signals.

Sweep imaging Fourier transform (SWIFT) is an efficient (fast and quiet) specialized magnetic resonance imaging (MRI) method for imaging tissues or organs that give only short-lived signals due to fast spin-spin relaxation rates. Based on the idea of compressed sensing, this paper proposes a novel method for further enhancing SWIFT using chaotic compressed sensing (CCS-SWIFT). With reduced number of measurements, CCS-SWIFT effectively faster than SWIFT. In comparison with a recently proposed chaotic compressed sensing method for standard MRI (CCS-MRI), simulation results showed that CCS-SWIFT outperforms CCS-MRI in terms of the normalized relative error in the image reconstruction and the probability of exact reconstruction.