Purpose: To detect small diagnostic signals such as lung nodules in chest radiographs, radiologists magnify a region-of-interest using linear interpolation methods. However, such methods tend to generate over-smoothed images with artifacts that can make interpretation difficult. The purpose of this study was to investigate the effectiveness of super-resolution methods for improving the image qu...

We have recently introduced a novel MRI methodology, so-called super resolution track-density imaging (TDI), which produces high-quality white matter images, with high spatial resolution and exquisite anatomical contrast not available from other MRI modalities. This method achieves super resolution by utilising the long-range information contained in the diffusion MRI fibre tracks. In this stud...

Second generation wavelet based super resolution algorithms developed in this research are presented in this paper. Generalization of the Second generation wavelet super resolution (SGWSR) algorithm to handle arbitrarily irregular 2-D grids as opposed to the semi-regular grids handled in the previous algorithm is introduced. A consequence is the ability to incorporate the projective camera moti...

This paper presents a novel technique to simultaneously estimate the depth and the focused image of a scene both at a super-resolution, from its defocused observations. Super-resolution refers to the generation of high resolution images from a sequence of low resolution images. Hitherto, the super-resolution technique has been restricted only to the intensity domain. In this paper, we extend th...

Dendritic spines and synapses are critical for neuronal communication, and they are perturbed in many neurological disorders; however, the study of these structures in living cells has been hindered by their small size. Super resolution microscopy, unlike conventional light microscopy, is diffraction unlimited and thus is well suited for imaging small structures, such as dendritic spines and sy...

In this paper, we propose a robust image super resolution algorithm, which aims to maximize the overall visual quality of super resolution results. We consider a good super resolution algorithm to be fidelity preserving, image detail enhancing and smooth. Accordingly, we define perception-based measures for these visual qualities. Based on these quality measures, we formulate image super resolu...

In this paper, we propose a robust image super-resolution algorithm, which aims to maximize the overall visual quality of super-resolution results. We consider a good super-resolution algorithm to be fidelity preserving, image detail enhancing and smooth. Accordingly, we define perception-based measures for these visual qualities. Based on these quality measures, we formulate image super-resolu...

Image super-resolution aims to reconstruct a high-resolution image from one or multiple low-resolution images which is an essential operation in a variety of applications. Due to the inherent ambiguity for superresolution, it is a challenging task to reconstruct clear, artifacts-free edges while still preserving rich and natural textures. In this paper, we propose a novel, straightforward, and ...

In a patch-based super-resolution algorithm, a lowresolution patch is influenced by surrounding patches due to blurring. We propose to remove this boundary effect by subtracting the blur from the surrounding high-resolution patches, which enables more accurate sparse representation. We demonstrate improved performance through experimentation. The proposed algorithm can be applied to most of pat...

Face images that are captured by surveillance cameras usually have a very low resolution, which significantly limits the performance of face recognition systems. In the past, super-resolution techniques have been proposed to increase the resolution by combining information from multiple images. These techniques use super-resolution as a preprocessing step to obtain a high-resolution image that ...