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.
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
.
DOI: 10.1007/s12204-017-1878-5
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