In image processing, noise is usually modeled as white Gaussian noise to represent general sensor and environmental clutter, and many effective methods have been developed to remove Gaussian noise. We show here that in many situations, such as Terahertz (ThZ) images or under-water images distorted by wavy surface, noise may be highly non-Gaussian, and even heavy-tailed with power-law distributions. We perceive that such noise may be ubiquitous, such as in images obtained by radar, LIDAR, satellite, and electro-optical visual cameras, in unsteady environments. We show that such noise cannot be effectively reduced by even the best method (block-matching 3D transformation, BM3D) for removing Gaussian noise. A fundamental issue arises of how to develop a proper framework to aptly deal with such non-Gaussian noise. We propose a viable new approach using power-law analysis, and evaluate its effectiveness using well-known images in computer vision community. We show that the new approach, which we call thresholding-median filtering and BM3D (TM-BM3D), works effective on all known types of noise, Gaussian, salt and pepper, and power-law noise.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Image denoising in the presence of non-Gaussian, power-law noise


    Beteiligte:
    Gao, Jianbo (Autor:in) / Chen, Qian (Autor:in) / Blasch, Erik (Autor:in)


    Erscheinungsdatum :

    01.07.2012


    Format / Umfang :

    4701283 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Image Denoising With Gaussian Mixture Model

    Cao, Yang / Luo, Yupin / Yang, Shiyuan | IEEE | 2008



    Image Denoising Using Complex Gaussian Scale Mixtures in Complex Curvelet Transform Domain

    Yan, H. / Li, G. / Zhang, X. | British Library Online Contents | 2009


    An Algorithm for Image Denoising with Automatic Noise Estimate

    Landi, G. | British Library Online Contents | 2009