An infrared image super-resolution reconstruction method based on compressive sensing |
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Affiliation: | 1. Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China;2. Key Laboratory of Opto-electronic Information Processing, Chinese Academy of Sciences, Shenyang 110016, China;3. University of Chinese Academy of Science, Beijing 100049, China;4. State Key Laboratory of Precision Measuring Technology & Instruments, Centre of MicroNano Manufacturing Technology, Tianjin University, Tianjin 300072, China;1. School of Mechanical Engineering, Guilin University of Electronic Technology, Guilin 541004, PR China;2. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, PR China;1. Dept. of Computer Science and Artificial Intelligence, University of Granada, Spain;2. Dept. of Languages and Information Systems, University of Granada, Spain;3. Dept. of Electrical Engineering and Computer Science, Northwestern University, USA |
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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. |
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Keywords: | Infrared image SRR CS Difference operation OMP |
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