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A Regularized Super Resolution Algorithm for Generalized Gaussian Noise |
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| Authors: | CHEN Wen FANG Xiang-zhong LIU Li-feng JIANG Wei DING Da-wei QIAO Yan-tao Institute of Image Communication Information Processing Shanghai Jiaotong University Shanghai China Computer Information Department Shanghai University of Electric Power Shanghai China Key Lab oratory of Intelligent Computing & Signal Processing Ministry of Education Anhui University Hefei China |
| Affiliation: | CHEN Wen1,FANG Xiang-zhong 1,LIU Li-feng 1,JIANG Wei2,DING Da-wei3,QIAO Yan-tao11 Institute of Image Communication , Information Processing,Shanghai Jiaotong University,Shanghai 200240,China2 Computer , Information Department,Shanghai University of Electric Power,Shanghai 200090,China3 Key Lab oratory of Intelligent Computing & Signal Processing,Ministry of Education,Anhui University,Hefei 230039,China |
| Abstract: | In this paper, an iterative regularized super resolution (SR) algorithin considering non-Gaussian noise is proposed. Based on the assumption of a generalized Gaussian distribution for the contaminating noise, an lp norm is adopted to measure the data fidelity term in the cost function. In the meantime, a regularization functional defined in terms of the desired high resolution (HR) image is employed, which allows for the simultaneous determination of its value and the partly reconstructed image at each iteration step. The convergence is thoroughly studied. Simulation results show the effectiveness of the proposed algorithm as well as its superiority to conventional SR methods. |
| Keywords: | super resolution generalized p-Gaussian distribution regularization parameter |
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