caa a22 4500 605540292 CHVBK 20210128100911.0 cr unu---uuuuu 210128e20150901xx s 000 0 eng 10.1007/s00371-014-1007-5 doi (NATIONALLICENCE)springer-10.1007/s00371-014-1007-5 A novel multi-image super-resolution reconstruction method using anisotropic fractional order adaptive norm [Elektronische Daten] [Chuanbo Chen, Hu Liang, Shengrong Zhao, Zehua Lyu, Mudar Sarem] 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. Springer-Verlag Berlin Heidelberg, 2014 Anisotropic fractional order adaptive norm nationallicence Multi-image super-resolution reconstruction nationallicence Gradient descent method nationallicence Chen Chuanbo School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut Liang Hu School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China aut Zhao Shengrong School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China aut Lyu Zehua School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut Sarem Mudar School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut The Visual Computer Springer Berlin Heidelberg 31/9(2015-09-01), 1217-1231 0178-2789 31:9<1217 2015 31 371 https://doi.org/10.1007/s00371-014-1007-5 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00371-014-1007-5 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Chen Chuanbo School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut NATIONALLICENCE 700 1- Liang Hu School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China aut NATIONALLICENCE 700 1- Zhao Shengrong School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China aut NATIONALLICENCE 700 1- Lyu Zehua School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut NATIONALLICENCE 700 1- Sarem Mudar School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China aut NATIONALLICENCE 773 0- The Visual Computer Springer Berlin Heidelberg 31/9(2015-09-01), 1217-1231 0178-2789 31:9<1217 2015 31 371