Blind Image Deblurring for Multiply Image Frames Based on an Iterative Algorithm

2016 ◽  
Vol 13 (10) ◽  
pp. 6531-6538
Author(s):  
Jia Ge ◽  
Peng Xianrong ◽  
Zhang Jianlin ◽  
Fu Chengyu
Keyword(s):  
Blind Deconvolution ◽  
Object Image ◽  
Current Frame ◽  
Blind Image Deblurring ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Iterative Solutions ◽  
Degraded Images ◽  
Blind Image

A novel algorithm based on an iterative and nonnegative algorithm has been developed for performing blind deconvolution on multiply degraded image frames. The algorithm naturally preserves the nonnegative constraint on the iterative solutions of blind deconvolution and can produce a restored image of high resolution. Furthermore, benefited from the interframe information, the neighbouring frame can be seen as degenerated from the same object image and different point spread function (PSF), so utilizing the result of the last frame to the initial estimate of the current frame can reduce iterative times and enhance the efficiency of the algorithm, meanwhile, the algorithm is free from the instability of numerical computation. Results of applying the algorithm to simulated and real degraded images are reported.

A Turbulence-Degraded Image Restoration Algorithm Using the Maximum Likelihood Estimation Method

2014 ◽  
Vol 543-547 ◽  
pp. 2391-2394
Author(s):  
Feng Wang ◽  
Kun Fan Zhang ◽  
Fan Kun Meng ◽  
Yong Jun Zhao
Keyword(s):  
Point Spread Function ◽  
Short Exposure ◽  
Computational Formula ◽  
Object Image ◽  
Point Spread ◽  
Restoration Algorithm ◽  
Spread Function ◽  
Degraded Image ◽  
Numerical Computing ◽  
Degraded Images

The RL(Richardson-Lucy) algorithm is an important method for restoration of turbulence-degraded images. However, the shortcoming of this method is that it tends to amplify the noise and exsits excessive smoothing in the iterative procedure. This paper discusses the RL algorithm and its improving methods focusing on turbulence-degraded images restoration.Firstly, a short exposure atmospheric turbulence-degraded model is established and a numerical computing method is proposed for random phase screen. Secondly, the essential principle and computational formula are deduced. To restore the object image effectively from the turbulence-degraded image, a new double-circulation iterative Richardson-Lucy restoration algorithm using TV-regularized method is proposed. This new algorithm introduces the total variation restraint and estimates the object image and the point spread function based on the inner and outer double-circulation iteration, which can use the inherent relation between the object image and the point spread function adequately. Simulation experiments show that the proposed algorithm can effectively preserve the details and edges of the image and its restoration effect is obviously better than the traditional RL algorithm.

scholarly journals Estimating the Size of the Point Spread Function Using Fuzzy Logic for Blind Restoration of Satellite Image

2017 ◽  
Vol 5 (4) ◽  
pp. 48
Author(s):  
Fouad Aouinti ◽  
M'barek Nasri ◽  
Mimoun Moussaoui
Keyword(s):  
Point Spread Function ◽  
Fuzzy Inference ◽  
Blind Deconvolution ◽  
Satellite Image ◽  
Inference System ◽  
Point Spread ◽  
Spread Function ◽  
Degraded Image ◽  
Blind Restoration ◽  
Image Edges

Despite the considerable progress in the field of imaging, the acquired image can undergo certain degradations which are mainly summarized in blur and noise. The objective of the restoration is to estimate from the observed image an image as close as possible to the original image. The iterative blind deconvolution (IBD) can be used effectively when no information about the distortion is known. This algorithm starts with a random initial estimate of the point spread function (PSF) whose its size affects strongly the restoration process of the degraded image. In this paper, we have implemented a fuzzy inference system (FIS) to determine the size of the PSF through the examination of the blurred satellite image edges and the measurement of the blur width in pixels around an obviously sharp object. The obtained results are encouraging, which confirms the good performance of the proposed approach.

scholarly journals Blind Deblurring Based on Sigmoid Function

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3484
Author(s):  
Shuhan Sun ◽  
Lizhen Duan ◽  
Zhiyong Xu ◽  
Jianlin Zhang
Keyword(s):  
Performance Optimization ◽  
Degradation Process ◽  
Image Deconvolution ◽  
Sigmoid Function ◽  
Blind Image Deblurring ◽  
Linear Estimators ◽  
Blind Deblurring ◽  
Degraded Image ◽  
Excellent Property ◽  
Blind Image

Blind image deblurring, also known as blind image deconvolution, is a long-standing challenge in the field of image processing and low-level vision. To restore a clear version of a severely degraded image, this paper proposes a blind deblurring algorithm based on the sigmoid function, which constructs novel blind deblurring estimators for both the original image and the degradation process by exploring the excellent property of sigmoid function and considering image derivative constraints. Owing to these symmetric and non-linear estimators of low computation complexity, high-quality images can be obtained by the algorithm. The algorithm is also extended to image sequences. The sigmoid function enables the proposed algorithm to achieve state-of-the-art performance in various scenarios, including natural, text, face, and low-illumination images. Furthermore, the method can be extended naturally to non-uniform deblurring. Quantitative and qualitative experimental evaluations indicate that the algorithm can remove the blur effect and improve the image quality of actual and simulated images. Finally, the use of sigmoid function provides a new approach to algorithm performance optimization in the field of image restoration.

Multichannel Blind Deconvolution-Based on-Orbit Estimation of Point Spread Function for Space Optical Remote Sensors

2013 ◽  
Vol 33 (4) ◽  
pp. 0428001 ◽  
Author(s):  
郭玲玲 Guo Lingling ◽  
吴泽鹏 Wu Zepeng ◽  
张立国 Zhang Liguo ◽  
任建岳 Ren Jianyue
Keyword(s):  
Point Spread Function ◽  
Blind Deconvolution ◽  
Remote Sensors ◽  
Point Spread ◽  
Spread Function ◽  
Orbit Estimation

Adaptive restoration method of multi-frame turbulence-degraded images based on stochastic point spread function

2015 ◽  
Vol 8 (3) ◽  
pp. 368-377
Author(s):  
朱瑞飞 ZHU Rei-fei ◽  
魏群 WEI Qun ◽  
王超 WANG Chao ◽  
贾宏光 JIA Hong-guang ◽  
吴海龙 WU Hai-long
Keyword(s):  
Point Spread Function ◽  
Point Spread ◽  
Adaptive Restoration ◽  
Spread Function ◽  
Restoration Method ◽  
Degraded Images
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