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Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding
[chapter]
2013
Lecture Notes in Computer Science
Preserved Fulltext
In this paper, we propose a new approach based on sparse coding for single textual image Super-Resolution (SR). ...
The proposed approach is able to build more representative dictionaries learned from a large training Low-Resolution/High-Resolution (LR/HR) patch pair database. ...
SISR Via Multiple Learned Dictionaries Based Sparse Coding To address the SISR problem by using SC, we divide the issue into two phases. ...
doi:10.1007/978-3-642-41184-7_45
fatcat:cd6qo2j2arf73oqcbae726oa6m
A Survey on Various Single Image Super Resolution Techniques
ENGLISH
2013
International Journal of Innovative Research in Science, Engineering and Technology
ENGLISH
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Superresolution is the process of recovering a high-resolution (HR) image from single image or multiple low-resolution (LR) images of the same scene . ...
Super-resolution from a single frame is play an important role in many computer vision systems. ...
IEEE/
2013
Multiple Learned
Dictionaries based
Clustered
Sparse Coding for
the
Super-Resolution
of Single Text
Image
Multiple learned
dictionaries
based on
clustered sparse
coding
IEEE ...
doi:10.15680/ijirset.2012.0102024
fatcat:t45xr2uapvcrdnzds7ldc37eta
Multiple Learned Dictionaries Based Clustered Sparse Coding for the Super-Resolution of Single Text Image
2013
2013 12th International Conference on Document Analysis and Recognition
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This paper addresses the problem of generating a super-resolved version of a low-resolution textual image by using Sparse Coding (SC) which suggests that image patches can be sparsely represented from ...
In order to enhance the learning performance and improve the reconstruction ability, we propose in this paper a multiple learned dictionaries based clustered SC approach for single text image superresolution ...
SISR VIA MULTIPLE LEARNED DICTIONARIES BASED CLUSTERED SPARSE CODING To address the SISR task by using SC, we divide the issue into two phases: the training phase and the reconstruction phase detailed ...
doi:10.1109/icdar.2013.103
dblp:conf/icdar/WalhaDLGA13
fatcat:73faybjtv5dszd62vokpvghoqe
Learning context-aware sparse representation for single image super-resolution
2011
2011 18th IEEE International Conference on Image Processing
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This paper presents a novel learning-based method for single image super-resolution (SR). ...
Unlike prior learning-based SR methods, our approach does not require the reoccurrence of similar image patches (within or across image scales), and we do not need to collect training low and high-resolution ...
INTRODUCTION Super-resolution (SR) is an inverse process of producing a high-resolution (HR) image from a single or multiple lowresolution (LR) inputs. ...
doi:10.1109/icip.2011.6115687
dblp:conf/icip/YangWHW11
fatcat:xxhve5rpj5gfzi7vzrjt2xhz6q
Multimodal sparse representation learning and applications
[article]
2016
arXiv
pre-print
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In particular, we propose the use of joint dictionary learning technique for sparse coding and formulate the joint representation for concision, cross-modal representations (in case of a missing modality ...
In this paper, we present a multimodal framework for learning sparse representations that can capture semantic correlation between modalities. ...
DEEP MULTIMODAL SPARSE CODING So far, we have only considered shallow learning architectures using a single layer of sparse coding and dictionary learning. ...
arXiv:1511.06238v3
fatcat:wt7cvmbpkrbrxbvxnyoydwtbqq
2020 Index IEEE Transactions on Circuits and Systems for Video Technology Vol. 30
2020
IEEE transactions on circuits and systems for video technology (Print)
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., +, TCSVT Feb. 2020 442-456
Image registration
RADAR: Robust Algorithm for Depth Image Super Resolution Based on
FRI Theory and Multimodal Dictionary Learning. ...
., +, TCSVT Sept. 2020 2947-2958
An Energy-Efficient FPGA-Based Deconvolutional Neural Networks Accel-
erator for Single Image Super-Resolution. ...
doi:10.1109/tcsvt.2020.3043861
fatcat:s6z4wzp45vfflphgfcxh6x7npu
CISP-BMEI 2020 TOC
2020
2020 13th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI)
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Chen ...........................................................................................................................................................................383 Single-Image Super-Resolution ...
Representation of Sound Speed Profiles based on Dictionary Learning Sijia Sun, Hangfang Zhao ... ...
doi:10.1109/cisp-bmei51763.2020.9263536
fatcat:7ulpvhnt35d2lg5dwzu4kexley
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 9532-9545 Soft-Edge Assisted Network for Single Image Super-Resolution. ...
.,
+, TIP 2020 5289-5300
Soft-Edge Assisted Network for Single Image Super-Resolution. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Spatio-spectral fusion of satellite images based on dictionary-pair learning
2014
Information Fusion
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Based on the sparse non-negative matrix factorization technique, this method first extracts spectral bases of LSHS and HSLS images by making full use of the rich spectral information in LSHS data. ...
This paper proposes a novel spatial and spectral fusion method for satellite multispectral and hyperspectral (or high-spectral) images based on dictionary-pair learning. ...
The application of dictionary-pair learning has been proved effective in image super-resolution [23, 24] . ...
doi:10.1016/j.inffus.2013.08.005
fatcat:abmplpth4fbctkcuoid6z54wpm
Table of contents
2020
IEEE Transactions on Image Processing
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Ren 1654 Receptive Field Size Versus Model Depth for Single Image Super-Resolution ....... R. Wang, M. Gong, and D. ...
Zhang 4643 Soft-Edge Assisted Network for Single Image Super-Resolution ............................. F. Fang, J. Li, and T. ...
doi:10.1109/tip.2019.2940372
fatcat:h23ul2rqazbstcho46uv3lunku
2021 Index IEEE Transactions on Multimedia Vol. 23
2021
IEEE transactions on multimedia
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., +, TMM 2021 52-63 Image Quality Assessment Using Kernel Sparse Coding. Zhou, Z., +, TMM 2021 1592-1604 Learned Multi-Resolution Variable-Rate Image Compression With Octave-Based Residual Blocks. ...
., +, TMM 2021 257-267 Weighted Adaptive Image Super-Resolution Scheme Based on Local Fractal Feature and Image Roughness. ...
Liao, J., and Kwong, S., Semantic Example Guided Image-to-Image Translation; TMM 2021 1654-1665 Huang, J., see Gong, X., TMM 2021 2820-2832 Huang, J., see Zhong, H., TMM 2021 1264 -1273 Huang, K., see ...
doi:10.1109/tmm.2022.3141947
fatcat:lil2nf3vd5ehbfgtslulu7y3lq
Zero-Shot Image Classification Using Coupled Dictionary Embedding
[article]
2021
arXiv
pre-print
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We use images from seen classes and semantic attributes from seen and unseen classes to learn two dictionaries that can represent sparsely the visual and semantic feature vectors of an image. ...
In this paper, we propose a new ZSL algorithm using coupled dictionary learning. ...
[72] for single image super-resolution problem [45] . ...
arXiv:1906.10509v2
fatcat:z52dppw445h6pjyp3nookjjpxq
Visual Quality Assessment for Super-resolved Images: Database and Method
2019
IEEE Transactions on Image Processing
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Image super-resolution (SR) has been an active research problem which has recently received renewed interest due to the introduction of new technologies such as deep learning. ...
Index Terms-Full reference, image database, image quality assessment, image super-resolution. ...
In this work, we only consider the image super-resolved from one single low-resolution (LR) input. Some authors use image SR as a synonym of image upscaling [4] . ...
doi:10.1109/tip.2019.2898638
fatcat:sg4qzem3s5b5dpzvrik3yinhxu
Superresolution Reconstruction of Video Based on Efficient Subpixel Convolutional Neural Network for Urban Computing
2020
Wireless Communications and Mobile Computing
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In this paper, the resolution of video is improved by superresolution reconstruction based on a learning method. ...
, where the optical flow is introduced in the deep learning network. ...
High-and low-resolution dictionaries are trained for LR image and HR image resolution, so that all low-resolution images to be reconstructed can extract sparse representation from dictionaries. ...
doi:10.1155/2020/8865110
fatcat:lrcb3uf5b5b4zotmwxwuufwsy4
TWIST-GAN: Towards Wavelet Transform and Transferred GAN for Spatio-Temporal Single Image Super Resolution
[article]
2021
arXiv
pre-print
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Recently, deep learning and generative adversarial networks(GANs) have made breakthroughs for the challenging task of single image super-resolution (SISR). ...
Single Image Super-resolution (SISR) produces high-resolution images with fine spatial resolutions from aremotely sensed image with low spatial resolution. ...
Further, to use sparse coding before assuming that an image might be a well-designed dictionary, the image will be a reasonable reference and is commonly used for sparse-code methods [38] [37] . ...
arXiv:2104.10268v1
fatcat:oznne3a7xjhbtej6wgkdf3og7i
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