Super-resolution using Hidden Markov model and Bayesian detection estimation framework

被引：0
|
作者：
Humblot, Fabrice [1 ,2 ]
Mohammad-Djafari, Ali [1 ]
机构：
[1] DGA/DET/SCET/CEP/ASC/GIP, Arcueil, 94114, France
[2] LSS/UMR8506 (CNRS-Supèlec-UPS), Gif-sur-Yvette Cedex, 91192, France
来源：
Eurasip Journal on Applied Signal Processing | 2006年 / 2006卷
关键词：
This paper presents a new method for super-resolution (SR)reconstruction of a high-resolution (HR) image from severallow-resolution (LR) images. The HR image is assumed to be composedof homogeneous regions. Thus; the a priori distribution of thepixels is modeled by a finite mixture model (FMM) and a PottsMarkov model (PMM) for the labels. The whole a priori model isthen a hierarchical Markov model. The LR images are assumed to beobtained from the HR image by lowpass filtering; arbitrarilytranslation; decimation; and finally corruption by a random noise.The problem is then put in a Bayesian detection and estimationframework; and appropriate algorithms are developed based onMarkov chain Monte Carlo (MCMC) Gibbs sampling. At the end; wehave not only an estimate of the HR image but also an estimate ofthe classification labels which leads to a segmentation result;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Journal article (JA)
引用
收藏
相关论文
共 50 条
  • [1] Super-Resolution Using Hidden Markov Model and Bayesian Detection Estimation Framework
    Fabrice Humblot
    Ali Mohammad-Djafari
    EURASIP Journal on Advances in Signal Processing, 2006
  • [2] Super-resolution using hidden Markov model and Bayesian detection estimation framework
    Humblot, Fabrice
    Mohammad-Djafari, Ali
    EURASIP JOURNAL ON APPLIED SIGNAL PROCESSING, 2006, 2006 (1)
  • [3] PARAMETER ESTIMATION IN BAYESIAN SUPER-RESOLUTION PANSHARPENING USING CONTOURLETS
    Amro, Israa
    Mateos, Javier
    Vega, Miguel
    2011 18TH IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING (ICIP), 2011, : 1345 - 1348
  • [4] Super-resolution and joint segmentation in Bayesian framework
    Humblot, F
    Mohammad-Djafari, A
    Bayesian Inference and Maximum Entropy Methods in Science and Engineering, 2005, 803 : 207 - 214
  • [5] Selective Bayesian estimation for efficient super-resolution
    Ivanovski, ZA
    Karam, LJ
    Abousleman, GP
    PROCEEDINGS OF THE FOURTH IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, 2004, : 433 - 436
  • [6] BAYESIAN SUPER-RESOLUTION PANSHARPENING USING CONTOURLETS
    Amro, I.
    Mateos, J.
    Vega, M.
    2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING, 2010, : 809 - 812
  • [7] Intrusion Detection System using Bayesian Network and Hidden Markov Model
    Devarakonda, Nagaraju
    Pamidi, Srinivasulu
    Kumari, Valli V.
    Govardhan, A.
    2ND INTERNATIONAL CONFERENCE ON COMPUTER, COMMUNICATION, CONTROL AND INFORMATION TECHNOLOGY (C3IT-2012), 2012, 4 : 506 - 514
  • [8] A Bayesian estimation approach to super-resolution reconstruction for face images
    Huang, Hua
    Fan, Xin
    Qi, Chun
    Zhu, Shihua
    ADVANCES IN MACHINE VISION, IMAGE PROCESSING, AND PATTERN ANALYSIS, 2006, 4153 : 406 - 415
  • [9] Super-resolution image reconstruction based on wavelet-domain classified hidden Markov tree model
    Lou, Shuai
    Ding, Zhenliang
    Yuan, Feng
    Li, Jing
    Shuju Caiji Yu Chuli/Journal of Data Acquisition and Processing, 2008, 23 (SUPPL.): : 77 - 80
  • [10] A Video Super-Resolution Framework Using SCoBeP
    Barzigar, Nafise
    Roozgard, Aminmohammad
    Verma, Pramode
    Cheng, Samuel
    IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, 2016, 26 (02) : 264 - 277
12345