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Employing structural and statistical information to learn dictionary(s) for single image super-resolution in sparse domain

  • Published:2016-10 Issue: Volume:48 Page:63-80
  • ISSN:0923-5965
  • Container-title:Signal Processing: Image Communication
  • language:en
  • Short-container-title:Signal Processing: Image Communication

Author:

Mandal Srimanta,Sao Anil Kumar

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Signal Processing,Software

Reference63 articles.

1. Super-resolution image reconstruction;Park;IEEE Signal Process. Mag.,2003

2. Cubic convolution interpolation for digital image processing;Keys;IEEE Trans. Acoust. Speech Signal Process.,1981

3. Cubic splines for image interpolation and digital filtering;Hou;IEEE Trans. Acoust. Speech Signal Process.,1978

4. Improving resolution by image registration;Irani;CVGIP: Graph. Models Image Process.,1991

5. Fast and robust multiframe super resolution;Farsiu;IEEE Trans. Image Process.,2004

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1. Anticancer Peptides Classification Using Kernel Sparse Representation Classifier;IEEE Access;2023

2. Combining Non-local Sparse and Residual Channel Attentions for Single Image Super-resolution Across Modalities;Communications in Computer and Information Science;2023

3. A Scale-Adaptive Super-Resolution Algorithm for Single Remote Sensing Image;IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium;2022-07-17

4. No-Reference Stereo Image Quality Assessment by Learning Dictionaries and Color Visual Characteristics;IEEE Access;2019

5. Color Image Super Resolution in Real Noise;Proceedings of the 11th Indian Conference on Computer Vision, Graphics and Image Processing;2018-12-18
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