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Scalable Super-Resolution Imaging
2006 IEEE International Conference on Systems, Man and Cybernetics, 2006In this study, we have developed a novel method to obtain high resolution images using an ordinary digital camera. Using successively taken images, our fusion algorithm has a profound effect in the quality of the image which contains richer independent pixels than the originals do.
Evrim Ozcelik +4 more
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Epitomic Image Super-Resolution
Proceedings of the AAAI Conference on Artificial Intelligence, 2016We propose Epitomic Image Super-Resolution (ESR) to enhance the current internal SR methods that exploit the self-similarities in the input. Instead of local nearest neighbor patch matching used in most existing internal SR methods, ESR employs epitomic patch matching that features robustness to noise, and both local and non-local patch
Yingzhen Yang +6 more
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Contour enhanced image super-resolution
Journal of Visual Communication and Image Representation, 2022Linhua Kong +3 more
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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
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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
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Image and Vision Computing, 2006
Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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Abstract The shortcomings in commonly used kernel-based super-resolution drive the study of improved super-resolution algorithms of higher quality. In the past years a wide range of very different approaches has been taken to improve super-resolution.
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Learning a Deep Convolutional Network for Image Super-Resolution
European Conference on Computer Vision, 2014Chao Dong +3 more
semanticscholar +1 more source
NTIRE 2017 Challenge on Single Image Super-Resolution: Dataset and Study
2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2017E. Agustsson, R. Timofte
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Image super-resolution: A comprehensive review, recent trends, challenges and applications
Information Fusion, 2023Bhawna Goyal
exaly