Edge and contrast preserving in total variation image denoising

被引:11
|
作者
Tang, Liming [1 ]
Fang, Zhuang [1 ]
机构
[1] Hubei Univ Nationalities, Sch Sci, Enshi 445000, Peoples R China
来源
EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING | 2016年
关键词
Total variation; Forward diffusion; Backward diffusion; Image denoising; Contrast; TOTAL VARIATION MINIMIZATION; ENHANCEMENT; DECOMPOSITION; RESTORATION;
D O I
10.1186/s13634-016-0315-5
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
Total variation (TV) regularization can very well remove noise and simultaneously preserve the sharp edges. But it has the drawback of the contrast loss in the restoration. In this paper, we first theoretically analyze the loss of contrast in the original TV regularization model, and then propose a forward-backward diffusion model in the framework of total variation, which can effectively preserve the edges and contrast in TV image denoising. A backward diffusion term based on a nonconvex and monotony decrease potential function is introduced in the TV energy, resulting in a forward-backward diffusion. In order to finely control the strength of the forward and backward diffusion, and separately design the efficient algorithm to numerically implement the forward and backward diffusion, we propose a two-step splitting method to iteratively solve the proposed model. We adopt the efficient projection algorithm in the dual framework to solve the forward diffusion in the first step, and then use the simple finite differences scheme to solve the backward diffusion to compensate the loss of contrast occurred in the previous step. At last, we test the models on both synthetic and real images. Compared with the classical TV, forward and backward diffusion (FBD), two-step methods (TSM), and TV-FF models, our model has the better performance in terms of peak signal-to-noise ratio (PSNR) and mean structural similarity (MSSIM) indexes.
引用
收藏
页码:1 / 21
页数:21
相关论文
共 50 条
  • [21] Weighting Wiener and Total Variation for Image Denoising
    Liu, Yun
    Luo, Bing
    Zhang, Zhicheng
    Zhu, Yanchun
    Wu, Shibin
    Xie, Yaoqin
    2016 IEEE INTERNATIONAL CONFERENCE ON INFORMATION AND AUTOMATION (ICIA), 2016, : 1479 - 1483
  • [22] Improved algorithms for total variation image denoising
    Chen-Liang, Li (chenli@guet.edu.cn), 2018, SHPMedia Sdn Bhd (COMPENDIUM VOL. 1):
  • [23] Adaptive Rates for Total Variation Image Denoising
    Ortelli, Francesco
    van de Geer, Sara
    JOURNAL OF MACHINE LEARNING RESEARCH, 2020, 21
  • [24] An ELU Network with Total Variation for Image Denoising
    Wang, Tianyang
    Qin, Zhengrui
    Zhu, Michelle
    NEURAL INFORMATION PROCESSING (ICONIP 2017), PT III, 2017, 10636 : 227 - 237
  • [25] Edge-Preserving Image Denoising Based on Lipschitz Estimation
    Jalil, Bushra
    Jalil, Zunera
    Fauvet, Eric
    Laligant, Olivier
    APPLIED SCIENCES-BASEL, 2021, 11 (11):
  • [26] A Parallel Edge Preserving Algorithm for Salt and Pepper Image Denoising
    Aldinucci, M.
    Spampinato, C.
    Drocco, M.
    Torquati, M.
    Palazzo, S.
    2012 3RD INTERNATIONAL CONFERENCE ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS, 2012, : 97 - 102
  • [27] Locally Adaptive Edge Preserving Filter for Radar Image Denoising
    Rubel, Andrey
    Lukin, Vladimir
    Shark, Lik-Kwan
    2017 5TH IEEE MICROWAVES, RADAR AND REMOTE SENSING SYMPOSIUM (MRRS), 2017, : 121 - 124
  • [28] Edge-preserving image denoising and estimation of discontinuous surfaces
    Gijbels, I
    Lambert, A
    Qiu, PH
    IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2006, 28 (07) : 1075 - 1087
  • [29] Bayesian Networks for Edge Preserving Salt and Pepper Image Denoising
    Faro, A.
    Giordano, D.
    Scarciofalo, G.
    Spampinato, C.
    2008 FIRST INTERNATIONAL WORKSHOPS ON IMAGE PROCESSING THEORY, TOOLS AND APPLICATIONS (IPTA), 2008, : 287 - 291
  • [30] Image denoising using hybrid model with edge preserving capability
    Chen, Bo
    Lai, Jianhuang
    Yuen, Pongchi
    2006 INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND SECURITY, PTS 1 AND 2, PROCEEDINGS, 2006, : 1779 - 1784