The image super-resolution reconstruction based on wavelet decomposition and Markov random field

2005 ◽  
Author(s):  
Hongjiu Tao ◽  
Keming Jia ◽  
Xiaojun Tong
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Wavelet Decomposition ◽  
Super Resolution ◽  
Markov Random ◽  
Image Super Resolution

High‐order Markov random field for single depth image super‐resolution

2017 ◽  
Vol 11 (8) ◽  
pp. 683-690 ◽  
Author(s):  
Elham Shabaninia ◽  
Ahmad Reza Naghsh‐Nilchi ◽  
Shohreh Kasaei
Keyword(s):  
Random Field ◽  
Markov Random Field ◽  
Super Resolution ◽  
High Order ◽  
Depth Image ◽  
Markov Random ◽  
Image Super Resolution

scholarly journals A Novel Bayesian Super-Resolution Method for Radar Forward-Looking Imaging Based on Markov Random Field Model

2021 ◽  
Vol 13 (20) ◽  
pp. 4115
Author(s):  
Ke Tan ◽  
Xingyu Lu ◽  
Jianchao Yang ◽  
Weimin Su ◽  
Hong Gu
Keyword(s):  
Random Field ◽  
Objective Function ◽  
Markov Random Field ◽  
Prior Information ◽  
Structural Characteristics ◽  
Super Resolution ◽  
Resolution Method ◽  
Markov Random ◽  
Shrinkage Method ◽  
Forward Looking

Super-resolution technology is considered as an efficient approach to promote the image quality of forward-looking imaging radar. However, super-resolution technology is inherently an ill-conditioned issue, whose solution is quite susceptible to noise. Bayesian method can efficiently alleviate this issue through utilizing prior knowledge of the imaging process, in which the scene prior information plays a pretty significant role in ensuring the imaging accuracy. In this paper, we proposed a novel Bayesian super-resolution method on the basis of Markov random field (MRF) model. Compared with the traditional super-resolution method which is focused on one-dimensional (1-D) echo processing, the MRF model adopted in this study strives to exploit the two-dimensional (2-D) prior information of the scene. By using the MRF model, the 2-D spatial structural characteristics of the imaging scene can be well described and utilized by the nth-order neighborhood system. Then, the imaging objective function can be constructed through the maximum a posterior (MAP) framework. Finally, an accelerated iterative threshold/shrinkage method is utilized to cope with the objective function. Validation experiments using both synthetic echo and measured data are designed, and results demonstrate that the new MAP-MRF method exceeds other benchmarking approaches in terms of artifacts suppression and contour recovery.

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