Limitation in the resolution of CCD image sensors has provided motivation to enhance the resolution of images. Super-resolution has been applied mainly to grayscale images, and producing a high-resolution color image from a single CCD sensor has not been discussed thoroughly. This work aims at producing a high-resolution color image directly from ”color mosaic” images obtained by a singleCCD wi...

Single image super-resolution aims to generate a high-resolution image from a single low-resolution image, which is of great significance in extensive applications. As an ill-posed problem, numerous methods have been proposed to reconstruct the missing image details based on exemplars or priors. In this paper, we propose a fast and simple single image super-resolution strategy utilizing patch-w...

In this paper, a new directionally adaptive, learning based, single image super resolution method using multiple direction wavelet transform, called Directionlets is presented. This method uses directionlets to effectively capture directional features and to extract edge information along different directions of a set of available high resolution images .This information is used as the training...

Hallucinating high frequency image details in single image superresolution is a challenging task. Traditional super-resolution methods tend to produce oversmoothed output images due to the ambiguity in mapping between low and high resolution patches. We build on recent success in deep learning based texture synthesis and show that this rich feature space can facilitate successful transfer and s...

Mosaicing and super resolution are two ways to combine information from multiple frames in video sequences. Mosaicing displays the information of multiple frames in a single panoramic image. Super-resolution uses regions which appear in multiple frames to improve resolution and reduce noise. Combining both methods enables to represent all the static information in the sequence in a compact and ...

Learning based super-resolution can recover high resolution image with high quality. However, building an interactive learning based super-resolution system for general images is extremely challenging. In this paper, we proposed a novel GPU-based Interactive Super-Resolution system through Neighbor Embedding (ISRNE). Random projection tree (RPtree) with manifold sampling is employed to reduce t...

Super-resolution algorithms are facing a number of issues, in that image registration is one of the major challenges. Here, we need to generate an improved resolution image that is reconstructed based on several geometrically warped, linearly blurred and noisy images. The ultimate goal is to compute a high-resolution image from low resolution image. For this an approach which makes use of geome...

In this paper, a two-stage super resolution method that combines the multi-frame and example-based methods is proposed. Traditionally, the multi-frame super resolution method only utilizes the continuity prior and complementary information among the low-resolution(LR) images with sub-pixel misalignment. While the example-based method digs the prior from abundant training images, but performs lo...

Super-resolution framework uses multiple noisy low resolution images from camera to generate a higher resolution image that has better spatial resolution than any of the available low resolution images. Super-resolution is an ill-posed problem. A inherent difficulty is the challenge of inverting the image observation model without amplifying the effect of noise in the measured data. Classically...

1047-3203/$ see front matter 2009 Published by doi:10.1016/j.jvcir.2009.03.008 * Corresponding author. Fax: +852 23628439. E-mail address: [email protected] (K.M. Lam Example-based super-resolution is a promising approach to solving the image super-resolution problem. However, the learning process can be slow and prediction can be inaccurate. In this paper, we present a novel learning-based ...