In multiple-image super-resolution, a high-resolution image is estimated from a number of lower-resolution images. This usually involves computing the parameters of a generative imaging model (such as geometric and photometric registration, and blur) and obtaining a MAP estimate by minimizing a cost function including an appropriate prior. Two alternative approaches are examined. First, both re...

Screen content has become one of the prominent mediums in increasingly connected world. With prevalence remote collaboration and communication such as virtual conferences online education, recent years have witnessed a dramatic increase data volume screen content. compression serves fundamental technology fostering storage, transmission, exhibition In this article, we target super-resolution co...

Since the first success of Dong et al., deep-learning-based approach has become dominant in field single-image super-resolution. This replaces all handcrafted image processing steps traditional sparse-coding-based methods with a deep neural network. In contrast to methods, which explicitly create high/low-resolution dictionaries, dictionaries are implicitly acquired as nonlinear combination mul...

Super-resolution (SR) image reconstruction is the process of combining several low resolution images into a single higher resolution image. There is a driving need for digital images of higher resolutions and quality. However, there is a limit to the spatial resolution that can be recorded by any digital device. Growing interest in super-resolution (SR) restoration of video sequences and the cl...

 Fast Single Image Super-resolution by Self-trained Filtering Dalong Li, Steven Simske HP Laboratories HPL-2011-94 Super-resolution; PSNR; filter; image restoration; image enhancement This paper introduces an algorithm to super-resolve an image based on a self-training filter (STF). As in other methods, we first increase the resolution by interpolation. The interpolated image has higher resolu...

We propose a super-resolution method that exploits selfsimilarities and group structural information of image patches using only one single input frame. The super-resolution problem is posed as learning the mapping between pairs of low-resolution and high-resolution image patches. Instead of relying on an extrinsic set of training images as often required in example-based super-resolution algor...

Preserving accuracy is a challenging issue in super resolution images. In this paper, we propose a new FFT based image registration algorithm and a sparse based super resolution algorithm to improve the accuracy of super resolution image. Given a low resolution image, our approach initially extracts the local descriptors from the input and then the local descriptors from the whole correlated im...

This paper describes a super-resolution technique based on interpolation of the high-frequency subband images obtained by discrete wavelet transform (DWT) and the input image. This technique uses DWT to decompose an image into different subband images. Then the high-frequency subband images and the low-resolution input image have been interpolated, followed by combining all these images to gene...

Super-resolution aims to produce a high-resolution image from a set of one or more low-resolution images by recovering or inventing plausible high-frequency image content. Typical approaches try to reconstruct a high-resolution image using the sub-pixel displacements of several lowresolution images, usually regularized by a generic smoothness prior over the high-resolution image space. Other me...