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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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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 ...
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 ...
Then, section 3 details the multiple learned dictionaries based clustered SC approach proposed for single text image SR. ...
doi:10.1109/icdar.2013.103
dblp:conf/icdar/WalhaDLGA13
fatcat:73faybjtv5dszd62vokpvghoqe
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 . ...
The SR image approaches reconstruct a single higher-resolution image from a set of given lower-resolution images . There is a basic need for digital images of higher resolutions and quality. ...
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
Single Textual Image Super-Resolution Using Multiple Learned Dictionaries Based Sparse Coding
[chapter]
2013
Lecture Notes in Computer Science
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In this paper, we propose a new approach based on sparse coding for single textual image Super-Resolution (SR). ...
Based on the assumption that patches of the same cluster live in the same subspace, we exploit for each local LR patch its similarity to clusters in order to adaptively select the appropriate learned dictionary ...
Introduction The problem of producing a HR image from an observed LR image is referred as Single Image Super-Resolution (SISR). ...
doi:10.1007/978-3-642-41184-7_45
fatcat:cd6qo2j2arf73oqcbae726oa6m
Image Transformation Based on Learning Dictionaries across Image Spaces
2013
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Obtained sparse representation (together with the learned target space dictionary) provides multiple constraints for each pixel of the target image to be estimated. ...
The contributions of our proposed framework are three-fold. (1) We propose a concept of coupled dictionary learning based on coupled sparse coding, which requires the sparse coefficient vectors of a pair ...
Instead of doing independent dictionary learning for source and target image spaces, we introduce the concept of coupled dictionary learning based on coupled sparse coding. ...
doi:10.1109/tpami.2012.95
pmid:22529324
fatcat:b26jvrj4w5g3tpt2ayqpru4nru
Image Upscaling Using Multiple Dictionaries of Natural Image Patches
[chapter]
2013
Lecture Notes in Computer Science
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patch pairs for each of the topics using sparse dictionary learning technique. ...
Our approach is based on the fundamental idea that a low-resolution (LR) image could be generated from any of the multiple possible high-resolution (HR) images. ...
Introduction The goal of Super Resolution (SR) methods is to reconstruct HR image from a single or multiple input LR images. ...
doi:10.1007/978-3-642-37431-9_22
fatcat:ln5iekasfrcfzpsrpe66ha74ku
Fast and accurate image upscaling with super-resolution forests
2015
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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The aim of single image super-resolution is to reconstruct a high-resolution image from a single low-resolution input. ...
In the experimental part, we demonstrate on standard benchmarks for single image super-resolution that our approach yields highly accurate state-of-the-art results, while being fast in both training and ...
Acknowledgment: This work was supported by the Austrian Science Foundation (FWF) project Advanced Learning for Tracking and Detection (I535-N23) and by the Austrian Research Promotion Agency (FFG) projects ...
doi:10.1109/cvpr.2015.7299003
dblp:conf/cvpr/SchulterLB15
fatcat:zij7bkxr4zhphel6uxgexlnzcy
Single image super resolution based on sparse representation via directionally structured dictionaries
2014
2014 22nd Signal Processing and Communications Applications Conference (SIU)
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In this thesis, we propose an algorithm of sparse representation using structurally directional dictionaries to super resolve a single low resolution input image. ...
In the reconstruction part, a LR input image comes in and all the features are coded sparsely with the most suitable directional LR dictionary; and the sparse coding coefficients are then used together ...
SUPER RESOLUTION VIA SPARSE REPRESENTATION
Introduction Obtaining a high-resolution (HR) image from the single low-resolution (LR) image is known as "single image super-resolution (SISR)". ...
doi:10.1109/siu.2014.6830580
dblp:conf/siu/FarhadifardANO14
fatcat:d27w235al5a3pedfpftjbtz76i
Single image super-resolution by directionally structured coupled dictionary learning
2016
EURASIP Journal on Image and Video Processing
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In this paper, a new algorithm is proposed based on coupled dictionary learning with mapping function for the problem of single-image super-resolution. ...
The invariance of the sparse representations is assumed for the task of super-resolution. ...
[9] propose the use of multiple patches based clustered dictionaries instead of a single universal one. In this mechanism, the authors studied the geometric properties of the image patches. ...
doi:10.1186/s13640-016-0141-6
fatcat:n2oisepjhnc57jskivqulfevfu
RGB-Guided Hyperspectral Image Upsampling
2015
2015 IEEE International Conference on Computer Vision (ICCV)
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The spatial upsampling stage is guided by a high resolution RGB image of the same scene, and the spectrum substitution stage utilizes sparse coding to locally refine the upsampled hyperspectral image through ...
On the contrary, latest imaging sensors capture a RGB image with resolution of multiple times larger than a hyperspectral image. ...
Learning the HR-LR exemplar In order to learn the HR-LR (High Resolution-Low Resolution) exemplars for learning based single image super-resolution, we prepare training examples by downsampling a high ...
doi:10.1109/iccv.2015.43
dblp:conf/iccv/KwonT15
fatcat:as5jhvihrva7bhfsasroir7u2q
Spatiotemporal Fusion of Remote Sensing Images with Structural Sparsity and Semi-Coupled Dictionary Learning
2016
Remote Sensing
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For remote sensing images, the cluster and joint structural sparsity of the sparse coefficients could be employed as a priori knowledge. ...
In this paper, a new optimization model is constructed with the semi-coupled dictionary learning and structural sparsity to predict the unknown high-resolution image from known images. ...
Author Contributions: Jingbo Wei, Peng Liu and Lizhe Wang wrote the main manuscript text. Jingbo Wei, Peng Liu and Weijing Song designed the technology of data collection and data processing. ...
doi:10.3390/rs9010021
fatcat:dlgw3jbzaffr7jvwavfiaz4iza
2020 Index IEEE Transactions on Image Processing Vol. 29
2020
IEEE Transactions on Image Processing
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., +, TIP 2020 8199-8212 Blind Deblurring of Text Images Using a Text-Specific Hybrid Dictionary. ...
., +, TIP 2020 9532-9545 Soft-Edge Assisted Network for Single Image Super-Resolution. ...
doi:10.1109/tip.2020.3046056
fatcat:24m6k2elprf2nfmucbjzhvzk3m
Front Matter: Volume 10615
2018
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
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The publisher is not responsible for the validity of the information or for any outcomes resulting from reliance thereon. ...
Please use the following format to cite material from these proceedings: Publication of record for individual papers is online in the SPIE Digital Library. ...
]
10615 34
Image fusion based on Bandelet and sparse representation [10615-80]
10615 35
Adaptive structured dictionary learning for image fusion based on group-sparse-
representation [10615-215] ...
doi:10.1117/12.2316542
fatcat:tdaw76jq6nehpnttiga2lcuhna
Learning Weighted Forest and Similar Structure for Image Super Resolution
2019
Applied Sciences
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Image super resolution (SR) based on example learning is a very effective approach to achieve high resolution (HR) image from image input of low resolution (LR). ...
In addition, a low rank constraint is imposed on the HR image patches in each cluster. ...
We are very grateful to the editor(s) and reviewers for suggestions and comments to greatly improve the quality of the paper.
Conflicts of Interest: The authors declare no conflict of interest. ...
doi:10.3390/app9030543
fatcat:sijukl3ckjb4tlyp6rzf2k3qoe
Seven Ways to Improve Example-Based Single Image Super Resolution
2016
2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
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In this paper we present seven techniques that everybody should know to improve example-based single image super resolution (SR): 1) augmentation of data, 2) use of large dictionaries with efficient search ...
The techniques are widely applicable and require no changes or only minor adjustments of the SR methods. ...
This work was supported by the European Research Council project VarCity (#273940). ...
doi:10.1109/cvpr.2016.206
dblp:conf/cvpr/TimofteRG16
fatcat:a52itfb32bep5egiqpv6hvkprq
Sparse Representation based Computed Tomography Images Reconstruction by Coupled Dictionary Learning Algorithm
2020
IET Image Processing
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This study proposes an improved super-resolution method for CT medical images in the sparse representation domain with dictionary learning. ...
The sparse coupled K-singular value decomposition (KSVD) algorithm is employed for dictionary learning purposes. ...
Wang and Shen [29] proposed a clustered sparse coding scheme based on multiple dictionaries, the image patches were sparsely coded across different dictionaries. ...
doi:10.1049/iet-ipr.2019.1312
fatcat:cod6jp7c3rc2pkogfmw43m5wku
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