Multi-frame image super-resolution makes use of a set of low-resolution images to reconstruct one or more high-resolution images. This paper presents a novel super-resolution algorithm that uses perceptually important content characteristics such as edges, texture, and brightness to improve visual quality. The superresolution algorithm introduces perceptually-adaptive constraint relaxation to o...

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 ...

Depth image super-resolution is an extremely challenging task due to the information loss in sub-sampling. Deep convolutional neural network have been widely applied to color image super-resolution. Quite surprisingly, this success has not been matched to depth super-resolution. This is mainly due to the inherent difference between color and depth images. In this paper, we bridge up the gap and...

High Resolution images can be reconstructed from several blurred, noisy and aliased low resolution images using a computational process know as super resolution reconstruction. Super resolution reconstruction is the process of combining several low resolution images into a single higher resolution image. In this paper we concentrate on a special case of super resolution problem where the wrap i...

Reconstruction-based super-resolution algorithms require very accurate alignment and good choice of filters to be effective. Often these requirements are hard to satisfy, for example, when we adopt optical flow as the motion model. In addition, the condition of having enough sub-samples may vary from pixel to pixel. In this paper, we propose an alternative super-resolution method based on image...