Nonlocal Markovian models for image denoising

被引:13
|
作者
Salvadeo, Denis H. P. [1 ,2 ]
Mascarenhas, Nelson D. A. [2 ,3 ]
Levada, Alexandre L. M. [2 ]
机构
[1] Sao Paulo State Univ, Dept Stat Appl Math & Computat, Rua 24A,1515, BR-13503013 Rio Claro, Brazil
[2] Univ Fed Sao Carlos, Dept Comp, Via Washington Luis,Km 235, BR-13565905 Sao Carlos, SP, Brazil
[3] Fac Campo Limpo Paulista, Grad Program Comp Sci, Rua Guatemala 170, BR-13231230 Campo Limpo Paulista, Brazil
基金
巴西圣保罗研究基金会;
关键词
image denoising; maximum pseudolikelihood estimation; Markov random fields; nonlocal patch-based approach; parameter estimation; RECONSTRUCTION; NOISE;
D O I
10.1117/1.JEI.25.1.013003
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
Currently, the state-of-the art methods for image denoising are patch-based approaches. Redundant information present in nonlocal regions (patches) of the image is considered for better image modeling, resulting in an improved quality of filtering. In this respect, nonlocal Markov random field (MRF) models are proposed by redefining the energy functions of classical MRF models to adopt a nonlocal approach. With the new energy functions, the pairwise pixel interaction is weighted according to the similarities between the patches corresponding to each pair. Also, a maximum pseudolikelihood estimation of the spatial dependency parameter (beta) for these models is presented here. For evaluating this proposal, these models are used as an a priori model in a maximum a posteriori estimation to denoise additive white Gaussian noise in images. Finally, results display a notable improvement in both quantitative and qualitative terms in comparison with the local MRFs. (C) 2016 SPIE and IS&T
引用
收藏
页数:20
相关论文
共 50 条
  • [1] Bilevel Parameter Learning for Nonlocal Image Denoising Models
    M. D’Elia
    J. C. De Los Reyes
    A. Miniguano-Trujillo
    [J]. Journal of Mathematical Imaging and Vision, 2021, 63 : 753 - 775
  • [2] Bilevel Parameter Learning for Nonlocal Image Denoising Models
    D'Elia, M.
    De los Reyes, J. C.
    Miniguano-Trujillo, A.
    [J]. JOURNAL OF MATHEMATICAL IMAGING AND VISION, 2021, 63 (06) : 753 - 775
  • [3] Nonlocal Image and Movie Denoising
    Antoni Buades
    Bartomeu Coll
    Jean-Michel Morel
    [J]. International Journal of Computer Vision, 2008, 76 : 123 - 139
  • [4] Nonlocal image and movie denoising
    Buades, Antoni
    Coll, Bartomeu
    Morel, Jean-Michel
    [J]. INTERNATIONAL JOURNAL OF COMPUTER VISION, 2008, 76 (02) : 123 - 139
  • [5] A Nonlocal Bayesian Image Denoising Algorithm
    Lebrun, M.
    Buades, A.
    Morel, J. M.
    [J]. SIAM JOURNAL ON IMAGING SCIENCES, 2013, 6 (03): : 1665 - 1688
  • [6] ANISOTROPICALLY FOVEATED NONLOCAL IMAGE DENOISING
    Foi, Alessandro
    Boracchi, Giacomo
    [J]. 2013 20TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP 2013), 2013, : 464 - 468
  • [7] Nonlocal Adaptive Image Denoising Model
    Sun, Xiaoli
    Xu, Chen
    Li, Andmin
    [J]. MATHEMATICAL PROBLEMS IN ENGINEERING, 2013, 2013
  • [8] Nonlocal oriented method for image denoising
    Gao, Yonghui
    Yang, Jie
    Guo, Lv
    [J]. OPTICAL ENGINEERING, 2011, 50 (03)
  • [9] An improved nonlocal image denoising method
    Tao, Tao
    Li, Siwen
    Liu, Maoshan
    [J]. BASIC & CLINICAL PHARMACOLOGY & TOXICOLOGY, 2019, 125 : 50 - 50
  • [10] A NONLOCAL APPROACH FOR SAR IMAGE DENOISING
    Parrilli, S.
    Poderico, M.
    Angelino, C. V.
    Scarpa, G.
    Verdoliva, L.
    [J]. 2010 IEEE INTERNATIONAL GEOSCIENCE AND REMOTE SENSING SYMPOSIUM, 2010, : 726 - 729