2011•Optics and Precision EngineeringRequires access

Realization of iterative blind image restoration by self deconvolution and increment Wiener filter

温博 WEN Bo, 张启衡 Zhang Qiheng, 张建林 ZHANG Jian-lin

Open publisher page 1 citations

Abstract

An iterative blind image restoration algorithm based on Self-deconvolution and Incremental Wiener Filter(SDIWF-IBD) is proposed.The self-deconvolution estimation for a Point Spread Function(PSF) is applied to Iterative Blind Deconvolution(IBD) to estimating exactly the frequency domain of the PSF.The incremental Wiener filter is used in the image estimation of IBD to keep the algorithm convergence steady.To further control the convergency,an in-iterative acceleration is suggested to control the speed of algorithm convergence and reduce total external iteration.Experimental results indicate that more details are recovered in the restoration image with few distortions,and the algorithm is converged to a small error quickly.It concludes that the SDIWF-IBD algorithm has good restoration ability at a fast and controllable convergency speed,and is fit for applications in real-time.

About this research paper

What this paper is about

An iterative blind image restoration algorithm based on Self-deconvolution and Incremental Wiener Filter(SDIWF-IBD) is proposed.The self-deconvolution estimation for a Point Spread Function(PSF) is applied to Iterative Blind Deconvolution(IBD) to estimating exactly the frequency domain of the PSF.The incremental Wiener filter is used in the image estimation of IBD to keep the algorithm convergence steady.To further control the convergency,an in-iterative acceleration is suggested to control the speed of algorithm convergence and reduce total external iteration.Experimental results indicate that more details are recovered in the restoration image with few distortions,and the algorithm is converged to a small error quickly.It concludes that the SDIWF-IBD algorithm has good restoration ability at a fast and controllable convergency speed,and is fit for applications in real-time.

Why it matters

OpenAlex reports 1 citations for this work. Citation counts describe recorded attention and do not establish research quality.

Key contribution

A contribution statement is not available in the OpenAlex record.

Method / approach

Method details are not available in the OpenAlex metadata.

Main findings

Findings are not separately available in the OpenAlex metadata.

Limitations

Limitations are not available in the OpenAlex metadata.

Applications

Application details are not available in the OpenAlex metadata.

Available abstract

An iterative blind image restoration algorithm based on Self-deconvolution and Incremental Wiener Filter(SDIWF-IBD) is proposed.The self-deconvolution estimation for a Point Spread Function(PSF) is applied to Iterative Blind Deconvolution(IBD) to estimating exactly the frequency domain of the PSF.The incremental Wiener filter is used in the image estimation of IBD to keep the algorithm convergence steady.To further control the convergency,an in-iterative acceleration is suggested to control the speed of algorithm convergence and reduce total external iteration.Experimental results indicate that more details are recovered in the restoration image with few distortions,and the algorithm is converged to a small error quickly.It concludes that the SDIWF-IBD algorithm has good restoration ability at a fast and controllable convergency speed,and is fit for applications in real-time.

Key concepts: Wiener deconvolution, Blind deconvolution, Deconvolution, Wiener filter, Image restoration, Point spread function, Convergence (economics), Algorithm

Related papers

Back to paper searchBrowse research topicsOriginal source
Realization of iterative blind image restoration by self deconvolution and increment Wiener filter — Research Paper | ScholarLens