Positively constrained total variation penalized image restoration
Refereed conference paper presented and published in conference proceedings

香港中文大學研究人員
替代計量分析
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其它資訊
摘要The total variation (TV) minimization models are widely used in image processing, mainly due to their remarkable ability in preserving edges. There are many methods for solving the TV model. These methods, however, seldom consider the positivity constraint one should impose on image-processing problems. In this paper we develop and implement a new approach for TV image restoration. Our method is based on the multiplicative iterative algorithm originally developed for tomographic image reconstruction. The advantages of our algorithm are that it is very easy to derive and implement under different image noise models and it respects the positivity constraint. Our method can be applied to various noise models commonly used in image restoration, such as the Gaussian noise model, the Poisson noise model, and the impulsive noise model. In the numerical tests, we apply our algorithm to deblur images corrupted by Gaussian noise. The results show that our method give better restored images than the forwardbackward splitting algorithm. © 2011 World Scientific Publishing Company.
著者Chan R.H., Liang H.-X., Ma J.
出版年份2011
月份4
日期1
卷號3
期次1-2
出版社World Scientific Publishing Co. Pte Ltd
出版地Singapore
頁次187 - 201
國際標準期刊號1793-5369
語言英式英語
關鍵詞maximum penalized likelihood, multiplicative iterative algorithms, positivity constraint, Total variation

上次更新時間 2020-17-10 於 01:30