Superresolution Reconstruction of Infrared Image Based on Selfadaptive Gradient Threshold
| Autor: | 王娴雅 Wang Xianya, 郑坚 Zheng Jian, 白俊奇 Bai Junqi, 赵春光 Zhao Chunguang |
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| Rok vydání: | 2012 |
| Předmět: |
Random field
Markov random field Iterative method business.industry Computer science ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION Reconstruction algorithm Pattern recognition Iterative reconstruction Function (mathematics) Atomic and Molecular Physics and Optics Maximum a posteriori estimation Noise (video) Artificial intelligence business |
| Zdroj: | ACTA PHOTONICA SINICA. 41:554-557 |
| ISSN: | 1004-4213 |
| Popis: | In the super-resolution image reconstruction,the model of Huber-markov random field is a common regularizing operator.Aiming at the unsatisfying effect of image reconstruction caused by fixed gradient threshold in the Huber function,a super-resolution reconstruction algorithm is proposed based on self-adaptive gradient threshold.The regularizing model is structured based on data item and regular item under the maximum a posteriori probability framework;the regularizing parameters are updated using the intermediate results via iterative method and can solve the selected problem of gradient threshold in the model of Huber-markov random field.Experimental results show,the improved algorithm can select the proper regularizing parameters based on local gratitude threshold and find the optimal result,recover detailed information and eliminate noise effectively. |
| Databáze: | OpenAIRE |
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