A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm

Verfasser / Beitragende:
[Chuanbo Chen, Hu Liang, Shengrong Zhao, Zehua Lyu, Mudar Sarem]
Ort, Verlag, Jahr:
2015
Enthalten in:
The Visual Computer, 31/9(2015-09-01), 1217-1231
Format:
Artikel (online)
ID: 605540292
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024 7 0 |a 10.1007/s00371-014-1007-5  |2 doi 
035 |a (NATIONALLICENCE)springer-10.1007/s00371-014-1007-5 
245 0 2 |a A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm  |h [Elektronische Daten]  |c [Chuanbo Chen, Hu Liang, Shengrong Zhao, Zehua Lyu, Mudar Sarem] 
520 3 |a A high-resolution image is obtained by fusing the information derived from blurred, sub-pixel shifted, and noisy low-resolution observations. In this paper, a novel regularization model based on an Anisotropic Fractional Order Adaptive (AFOA) norm is proposed and then we apply the AFOA model into the Super-Resolution Reconstruction technology. Compared with the existing models, the proposed AFOA model can remove the noise and protect the edges adaptively according to the local features of the images. Meanwhile, the proposed AFOA model can avoid the staircase effect effectively in the smooth region. To obtain the solution to the proposed AFOA model, the Gradient Descent Method is used in this paper. Finally, the experimental results show that the proposed method has much improvement than the existing methods in the respect of the Peak Signal-to-Noise Ratio and the visual quality. 
540 |a Springer-Verlag Berlin Heidelberg, 2014 
690 7 |a Anisotropic fractional order adaptive norm  |2 nationallicence 
690 7 |a Multi-image super-resolution reconstruction  |2 nationallicence 
690 7 |a Gradient descent method  |2 nationallicence 
700 1 |a Chen  |D Chuanbo  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
700 1 |a Liang  |D Hu  |u School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
700 1 |a Zhao  |D Shengrong  |u School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
700 1 |a Lyu  |D Zehua  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
700 1 |a Sarem  |D Mudar  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
773 0 |t The Visual Computer  |d Springer Berlin Heidelberg  |g 31/9(2015-09-01), 1217-1231  |x 0178-2789  |q 31:9<1217  |1 2015  |2 31  |o 371 
856 4 0 |u https://doi.org/10.1007/s00371-014-1007-5  |q text/html  |z Onlinezugriff via DOI 
898 |a BK010053  |b XK010053  |c XK010000 
900 7 |a Metadata rights reserved  |b Springer special CC-BY-NC licence  |2 nationallicence 
908 |D 1  |a research-article  |2 jats 
949 |B NATIONALLICENCE  |F NATIONALLICENCE  |b NL-springer 
950 |B NATIONALLICENCE  |P 856  |E 40  |u https://doi.org/10.1007/s00371-014-1007-5  |q text/html  |z Onlinezugriff via DOI 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Chen  |D Chuanbo  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Liang  |D Hu  |u School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Zhao  |D Shengrong  |u School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Lyu  |D Zehua  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
950 |B NATIONALLICENCE  |P 700  |E 1-  |a Sarem  |D Mudar  |u School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China  |4 aut 
950 |B NATIONALLICENCE  |P 773  |E 0-  |t The Visual Computer  |d Springer Berlin Heidelberg  |g 31/9(2015-09-01), 1217-1231  |x 0178-2789  |q 31:9<1217  |1 2015  |2 31  |o 371