Results 261 to 270 of about 80,925 (302)
Single Image Super-Resolution [PDF]
Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal, 2019 Super-Resolution (SR) of a single image is a classic problem in computer vision. The goal of image super-resolution is to produce a high-resolution image from a low-resolution image. This paper presents a popular model, super-resolution convolutional neural network (SRCNN), to solve this problem. This paper also examines an improvement to SRCNN using a
Song, Yujing, Yujing Song
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Structure preserving single image super-resolution [PDF]
2016 IEEE International Conference on Image Processing (ICIP), 2016 In this paper, we present a novel structure preserving method for single image super-resolution to well construct edge structures and small detail structures. In our approach, the sharp edges are recovered via a novel edge preserving interpolation technique based on a well estimated gradient field and the edge preserving method, which incorporate the ...
Fan Yang 0053 +5 more
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Colorization for Single Image Super Resolution [PDF]
, 2010 This paper introduces a new procedure to handle color in single image super resolution (SR). Most existing SR techniques focus primarily on enforcing image priors or synthesizing image details; less attention is paid to the final color assignment. As a result, many existing SR techniques exhibit some form of color aberration in the final upsampled ...
Shuaicheng Liu +3 more
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Single Image Super-Resolution With Multiscale Similarity Learning
IEEE Transactions on Neural Networks and Learning Systems, 2013Example learning-based image super-resolution (SR) is recognized as an effective way to produce a high-resolution (HR) image with the help of an external training set. The effectiveness of learning-based SR methods, however, depends highly upon the consistency between the supporting training set and low-resolution (LR) images to be handled.
Kaibing Zhang, Xinbo Gao, Dacheng Tao
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A Unified Learning Framework for Single Image Super-Resolution
IEEE Transactions on Neural Networks and Learning Systems, 2014It has been widely acknowledged that learning- and reconstruction-based super-resolution (SR) methods are effective to generate a high-resolution (HR) image from a single low-resolution (LR) input. However, learning-based methods are prone to introduce unexpected details into resultant HR images.
Xinbo Gao, Dacheng Tao, Xuelong Li
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Single Image Super-Resolution for Medical Image Applications
2020In medical imaging, high-resolution images are expected to have the ability to deliver a more precise diagnosis with the practical application of high-resolution displays. This research proposes a deep learning method for single image super-resolution that learns an end-to-end mapping between the low and high-resolution images.
Tamarafinide V. Dittimi, Ching Y. Suen
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Single image super-resolution in frequency domain
2012 IEEE Southwest Symposium on Image Analysis and Interpretation, 2012This paper presents a neighborhood dependent components based feature learning (NDCFL) for regression analysis in single image super-resolution. Given a low resolution input, the method uses directional Fourier phase feature components to adaptively learn the regression kernel based on local covariance to estimate the high resolution image.
Mohammad Moinul Islam +3 more
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Pairwise Operator Learning for Patch-Based Single-Image Super-Resolution [PDF]
IEEE Transactions on Image Processing, 2017 Motivated by the fact that image patches could be inherently represented by matrices, single-image super-resolution is treated as a problem of learning regression operators in a matrix space in this paper. The regression operators that map low-resolution
Ling Shao
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