An Infrared Image Super-resolution Reconstruction Method Based on Compressive Sensing

DOI

10.2991/icmmct-16.2016.244How to use a DOI?

Keywords

Infrared image, SRR, CS, difference operation, OMP

Abstract

Limited by the properties of infrared detector and camera lens, infrared images are often detail missing and indistinct in vision. The spatial resolution needs to be improved to satisfy the requirements of practical application. Based on compressive sensing (CS) theory, this thesis presents a single image super-resolution reconstruction (SRR) method. With synthetically adopting image degradation model, difference operation-based sparse transformation method and orthogonal matching pursuit (OMP) algorithm, the image SRR problem is transformed into a sparse signal reconstruction issue in CS theory. In our work, the sparse transformation matrix is obtained through difference operation to image, and, the measurement matrix is achieved analytically from the imaging principle of infrared camera. Therefore, the time consumption can be decreased compared with the redundant dictionary obtained by sample training such as K-SVD. The experimental results show that our method can achieve favorable performance and good stability with low algorithm complexity.

Copyright

© 2016, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yuxing Mao
AU  - Yan Wang
AU  - Jintao Zhou
AU  - Haiwei Jia
PY  - 2016/03
DA  - 2016/03
TI  - An Infrared Image Super-resolution Reconstruction Method Based on Compressive Sensing
BT  - Proceedings of the 2016 4th International Conference on Machinery, Materials and Computing Technology
PB  - Atlantis Press
SP  - 1242
EP  - 1249
SN  - 2352-5401
UR  - https://doi.org/10.2991/icmmct-16.2016.244
DO  - 10.2991/icmmct-16.2016.244
ID  - Mao2016/03
ER  -