A weight matrix based blind Super Resolution restoration algorithm

2008 ◽  
Author(s):  
Suyu Wang ◽  
Li Zhuo ◽  
Lansun Shen
Keyword(s):  
Super Resolution ◽  
Weight Matrix ◽  
Restoration Algorithm

Super-Resolution Restoration for Image Based on Entropy Constraint and Projection onto Convex Set

2012 ◽  
Vol 468-471 ◽  
pp. 1041-1048 ◽  
Author(s):  
Xiao Qin Li ◽  
Kang Ling Fang ◽  
Can Jin
Keyword(s):  
Convergence Rate ◽  
Millimeter Wave ◽  
Convex Set ◽  
Super Resolution ◽  
Reconstruction Algorithm ◽  
Spatial Domain ◽  
Imaging Systems ◽  
Resolution Limit ◽  
Classical Algorithm ◽  
Restoration Algorithm

Super-resolution reconstruction for image breaks through the resolution limit of imaging systems without hardware change. The algorithm of projection onto convex set (POCS) is a typical super-resolution reconstruction algorithm in spatial domain. The classical algorithm of POCS lacks the overall constraint for the image, and the convergence rate for iteration is incontrollable. A new super-resolution restoration algorithm for image based on entropy constraint and POCS is proposed in this paper, and experiments with optical and millimeter wave images demonstrate that the new algorithm is effective in improving the precision of super-resolution restoration.

scholarly journals A New Image Super-Resolution Restoration Algorithm

2017 ◽  
Vol 173 (10) ◽  
pp. 5-12
Author(s):  
Sayed A. ◽  
A. S. ◽  
Ayman H. ◽  
A. K.
Keyword(s):  
Super Resolution ◽  
Restoration Algorithm ◽  
Image Super Resolution

scholarly journals Regularized super-resolution restoration algorithm for single medical image based on fuzzy similarity fusion

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Xingying Li ◽  
Weina Fu
Keyword(s):  
Multiple Scales ◽  
Medical Images ◽  
Signal To Noise Ratio ◽  
Nonlinear Function ◽  
Optimal Solution ◽  
Super Resolution ◽  
Regional Features ◽  
Fuzzy Similarity ◽  
Restoration Algorithm ◽  
Local Edge

Abstract Medical images are blurred and noised due to various reasons in the acquirement, transmission and storage. In order to improve the restoration quality of medical images, a regular super-resolution restoration algorithm based on fuzzy similarity fusion is proposed. Based on maintained similarity in multiple scales, the fused similarity of the medical images is computed by fuzzy similarity fusion. First, fuzzy similarity is determined by the regional features. The images with certain similarity are obtained according to the maximum value, and the fused image is obtained by all obvious regional features. Then, an adaptive regularized restoration algorithm is employed. In order to ensure the objective function has a global optimal solution, regularized parameters of the global minimum solution of nonlinear function are solved iteratively. Finally, experimental results show that mean square error (MSE) and peak signal-to-noise ratio (PSNR) of the restored image are visibly improved. The restored image also has an obvious improvement in the burr of local edge. Moreover, the algorithm has good stability with significantly enhanced PSNR.

Super-Resolution by POCS-SIFT Approach

2014 ◽  
Vol 519-520 ◽  
pp. 562-567
Author(s):  
Xiao Qin Li ◽  
Kang Ling Fang
Keyword(s):  
Convex Sets ◽  
Super Resolution ◽  
Scale Invariant ◽  
Sift Algorithm ◽  
Projection Onto Convex Sets ◽  
Invariant Feature ◽  
Restoration Algorithm ◽  
Blurred Image ◽  
Scale Invariant Feature ◽  
Sets Theory

Projection Onto Convex Sets theory (POCS) and Scale Invariant Feature Transform (SIFT) algorithm were introduced for super-resolution restoration of moving blurred image. In order to achieve a better restored image, a POCS-SIFT based super-resolution image restoration algorithm was proposed, which incorporates POCS theory and SIFT algorithm. From experimental results, the improved restored images are obtained by POCS-SIFT hybrid algorithm.

scholarly journals Research on region selection super resolution restoration algorithm based on infrared micro-scanning optical imaging model

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jian Chen ◽  
Yan Li ◽  
LiHua Cao
Keyword(s):  
Optical Model ◽  
Infrared Imaging ◽  
Infrared Image ◽  
Super Resolution ◽  
Small Target ◽  
Image Processing Algorithm ◽  
Detection Range ◽  
Low Snr ◽  
Restoration Algorithm ◽  
Low Power Dissipation

AbstractWith spring up of infrared imaging related industry, infrared imaging technology has become mainstream development direction of intelligent photoelectrical detection due to its good concealment, wide detection range, high positioning accuracy, long distant penetration, light weight, little volume, low power dissipation and high solidity. However, the features of infrared dim-small target image such as less details and low SNR become bottleneck of infrared image application. How to enhance imaging effect of infrared dim-small target becomes research hotspot. Starting from the point of ‘restoration as foundation’, the theory and technology of infrared dim-small target super-resolution restoration by utilizing the theory and technology of super-resolution restoration are explored in this paper. This paper mainly focuses on the research of super-resolution restoration algorithm of infrared dim-small target based on infrared micro-scanning optical model. Aiming at solving super-resolution restoration problem of infrared dim-small target, the traditional super-resolution restoration algorithm is optimized and the improved algorithm is proposed. Meanwhile, infrared micro-scanning optical model is introduced to break theoretical limit of simple image processing algorithm. And the performance of infrared image super-resolution restoration is improved.

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