Add to ORKGThe main objective of this project is the study of a learning based method for super-resolving low resolution images. The domain specific prior is incorporated into super resolution by the means of learning based estimation of missing details. Images are decomposed into fixed size patches in order to deal with time and space complexity. The problem is modeled by Markov Random Field which enforces resulting images to be spatially consistent. The spatial interactions are coupled with a similarity constraint which should be established between high-resolution training image patches and low resolution observations.
Abstract-In this paper, we address the problem of generating high-resolution (HR) image from a singl...
Dictionary learning and sparse representation are efficient methods for single-image super-resolutio...
In sparse representation based super-resolution, high resolution image is estimated from a single lo...
Super-resolution refers to the process of obtaining a high resolution image from one or more low res...
We propose a technique for super-resolution imaging of a scene from observations at different camera...
In this paper we propose a zoom based technique to super resolve static scene using observation capt...
Super Resolution of an image is one of the image processing methods that helps us in estimating the ...
Learning-based superresolution (SR) is a popular SR technique that uses application dependent priors...
Example learning-based image super-resolution (SR) is recognized as an effective way to produce a hi...
Example learning-based image super-resolution (SR) is recognized as an effective way to produce a hi...
In this paper single image superresolution problem using sparse data representation is described. Im...
The goal of learning-based image super resolution (SR) is to generate a plausible and visually pleas...
Image super-resolution refers to the process by which a higher-resolution enhanced image is synthesi...
Example-based super-resolution has become increasingly popular over the last few years for its abili...
It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) method...
Abstract-In this paper, we address the problem of generating high-resolution (HR) image from a singl...
Dictionary learning and sparse representation are efficient methods for single-image super-resolutio...
In sparse representation based super-resolution, high resolution image is estimated from a single lo...
Super-resolution refers to the process of obtaining a high resolution image from one or more low res...
We propose a technique for super-resolution imaging of a scene from observations at different camera...
In this paper we propose a zoom based technique to super resolve static scene using observation capt...
Super Resolution of an image is one of the image processing methods that helps us in estimating the ...
Learning-based superresolution (SR) is a popular SR technique that uses application dependent priors...
Example learning-based image super-resolution (SR) is recognized as an effective way to produce a hi...
Example learning-based image super-resolution (SR) is recognized as an effective way to produce a hi...
In this paper single image superresolution problem using sparse data representation is described. Im...
The goal of learning-based image super resolution (SR) is to generate a plausible and visually pleas...
Image super-resolution refers to the process by which a higher-resolution enhanced image is synthesi...
Example-based super-resolution has become increasingly popular over the last few years for its abili...
It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) method...
Abstract-In this paper, we address the problem of generating high-resolution (HR) image from a singl...
Dictionary learning and sparse representation are efficient methods for single-image super-resolutio...
In sparse representation based super-resolution, high resolution image is estimated from a single lo...