Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing

doi:10.1007/s12204-017-1878-5

上海交通大学学报（英文版） ›› 2017, Vol. 22 ›› Issue (5): 609-614.doi: 10.1007/s12204-017-1878-5

Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing

LIU Hongyi^a* (刘红毅), ZHANG Zhengrong^a (张峥嵘), XIAO Liang^b (肖亮), WEI Zhihui^b (韦志辉)

(a. School of Science; b. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China)

出版日期:2017-09-30 发布日期:2017-09-30
通讯作者: LIU Hongyi (刘红毅) E-mail: hyliu@njust.edu.cn

Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing

LIU Hongyi^a* (刘红毅), ZHANG Zhengrong^a (张峥嵘), XIAO Liang^b (肖亮), WEI Zhihui^b (韦志辉)

(a. School of Science; b. School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China)

Online:2017-09-30 Published:2017-09-30
Contact: LIU Hongyi (刘红毅) E-mail: hyliu@njust.edu.cn

摘要/Abstract

摘要： An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.

关键词: Poisson noise, nonlocal total variation (NLTV), Euler’s elastica (EE), variation regularization

Abstract: An enhancement-based Poisson denoising method for photon-limited images is presented. The noisy image is firstly pre-processed for enhancing incomplete object information, and then it is denoised while preserving the restored structural details. A variational regularization model based on Euler’s elastica (EE) is proposed for image enhancement pre-processing. A nonlocal total variation (NLTV) regularization model is then employed in the second stage of image denoising. The above two optimization problems are solved by the alternating direction method of multipliers (ADMM). For Poissonian images with low image peak values, experiments demonstrate the validity and efficiency of the proposed method for both restoring geometric structure and removing noise.

Key words: Poisson noise, nonlocal total variation (NLTV), Euler’s elastica (EE), variation regularization

中图分类号:

TP 391

LIU Hongyia* (刘红毅), ZHANG Zhengronga (张峥嵘), XIAO Liangb (肖亮), WEI Zhihuib (韦志辉). Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing[J]. 上海交通大学学报（英文版）, 2017, 22(5): 609-614.

LIU Hongyia* (刘红毅), ZHANG Zhengronga (张峥嵘), XIAO Liangb (肖亮), WEI Zhihuib (韦志辉). Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing[J]. Journal of shanghai Jiaotong University (Science), 2017, 22(5): 609-614.

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Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing

Poisson Noise Removal Based on Nonlocal Total Variation with Euler’s Elastica Pre-processing

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