An Infrared Image Super-resolution Reconstruction Method Based on Compressive Sensing
Yuxing Mao, Yan Wang, Jintao Zhou, Haiwei Jia
Available Online March 2016.
- https://doi.org/10.2991/icmmct-16.2016.244How to use a DOI?
- Infrared image, SRR, CS, difference operation, OMP
- 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.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
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 - https://doi.org/10.2991/icmmct-16.2016.244 ID - Mao2016/03 ER -