research-article

Super-resolving a Single Intensity/Range Image via Non-local Means and Sparse Representation

Authors:

Srimanta Mandal,

Anil K. SaoAuthors Info & Claims

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

Article No.: 58, Pages 1 - 8

https://doi.org/10.1145/2683483.2683541

Published: 14 December 2014 Publication History

Abstract

We propose an example-based super-resolution (SR) framework, which uses a single input image and, unlike most of the SR methods does not need an external high resolution (HR) dataset. Our SR approach is based in sparse representation framework, which depends on a dictionary, learned from the given test image across different scales. In addition, our sparse coding focuses on the detail information of the image patches. Furthermore, in the above process we have considered non-local combination of similar patches in the input image, which assist us to improve the quality of the SR result. We demonstrate the effectiveness of our approach for intensity images as well as range images. Contemplating the importance of edges in images of both these modalities, we have added an edge preserving constraint that will maintain the continuity of edge related information to the input low resolution image. We investigate the performance of our approach by rigorous experimental analysis and it shows to perform better than some state-of-the-art SR approaches.

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Cited By

Mandal SPurohit KRajagopalan A(2018)Color Image Super Resolution in Real NoiseProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293414(1-9)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1145/3293353.3293414
Chawdhary AKumari SBhavsar AVerma R(2018)No Reference Evaluation in Super-Resolution for CCTV Footage2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)10.1109/ICIINFS.2018.8721319(107-112)Online publication date: Dec-2018
https://doi.org/10.1109/ICIINFS.2018.8721319
Mandal SRajagopalan A(2018)Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse DomainComputer Vision, Pattern Recognition, Image Processing, and Graphics10.1007/978-981-13-0020-2_15(163-176)Online publication date: 26-Apr-2018
https://doi.org/10.1007/978-981-13-0020-2_15
Show More Cited By

Index Terms

Super-resolving a Single Intensity/Range Image via Non-local Means and Sparse Representation
1. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
      2. Computer vision representations
        Image representations
  2. Computer graphics
    1. Image manipulation

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Published In

cover image ACM Other conferences

ICVGIP '14: Proceedings of the 2014 Indian Conference on Computer Vision Graphics and Image Processing

December 2014

692 pages

ISBN:9781450330619

DOI:10.1145/2683483

General Chairs:
A. G. Ramakrishnan
IISc, Bangalore
,
Jitendra Malik
University California, Berkeley
,
Program Chairs:
Alex Efros
UC-Berkeley
,
C. V. Jawahar
IIIT Hyderabad
,
Manik Varma
Microsoft Research

Copyright © 2014 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 14 December 2014

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ICVGIP '14

ICVGIP '14: Indian Conference on Computer Vision Graphics and Image Processing

December 14 - 18, 2014

Bangalore, India

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Overall Acceptance Rate 95 of 286 submissions, 33%

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Cited By

Mandal SPurohit KRajagopalan A(2018)Color Image Super Resolution in Real NoiseProceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing10.1145/3293353.3293414(1-9)Online publication date: 18-Dec-2018
https://dl.acm.org/doi/10.1145/3293353.3293414
Chawdhary AKumari SBhavsar AVerma R(2018)No Reference Evaluation in Super-Resolution for CCTV Footage2018 IEEE 13th International Conference on Industrial and Information Systems (ICIIS)10.1109/ICIINFS.2018.8721319(107-112)Online publication date: Dec-2018
https://doi.org/10.1109/ICIINFS.2018.8721319
Mandal SRajagopalan A(2018)Single Noisy Image Super Resolution by Minimizing Nuclear Norm in Virtual Sparse DomainComputer Vision, Pattern Recognition, Image Processing, and Graphics10.1007/978-981-13-0020-2_15(163-176)Online publication date: 26-Apr-2018
https://doi.org/10.1007/978-981-13-0020-2_15
Mandal SBhavsar ASao A(2017)Depth Map Restoration From Undersampled DataIEEE Transactions on Image Processing10.1109/TIP.2016.262141026:1(119-134)Online publication date: 1-Jan-2017
https://dl.acm.org/doi/10.1109/TIP.2016.2621410
Mandal SBhavsar ASao A(2017)Noise adaptive super-resolution from single image via non-local mean and sparse representationSignal Processing10.1016/j.sigpro.2016.09.017132(134-149)Online publication date: Mar-2017
https://doi.org/10.1016/j.sigpro.2016.09.017
Kaur PMandal SSao A(2017)Significance of Magnetic Resonance Image Details in Sparse Representation Based Super ResolutionMedical Image Understanding and Analysis10.1007/978-3-319-60964-5_53(605-615)Online publication date: 22-Jun-2017
https://doi.org/10.1007/978-3-319-60964-5_53
Mandal SKumari SBhavsar ASao A(2016)Multi-scale image denoising while preserving edges in sparse domain2016 6th European Workshop on Visual Information Processing (EUVIP)10.1109/EUVIP.2016.7764583(1-6)Online publication date: Oct-2016
https://doi.org/10.1109/EUVIP.2016.7764583

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