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      Noise Reduction for CFA Image Sensors Exploiting HVS Behaviour

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          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          This paper presents a spatial noise reduction technique designed to work on CFA (Color Filtering Array) data acquired by CCD/CMOS image sensors. The overall processing preserves image details using some heuristics related to the HVS (Human Visual System); estimates of local texture degree and noise levels are computed to regulate the filter smoothing capability. Experimental results confirm the effectiveness of the proposed technique. The method is also suitable for implementation in low power mobile devices with imaging capabilities such as camera phones and PDAs.

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          Image quality assessment: from error visibility to structural similarity.

          Zhou Wang, Alan Conrad Bovik, Hamid Sheikh … (2004)
          Objective methods for assessing perceptual image quality traditionally attempted to quantify the visibility of errors (differences) between a distorted image and a reference image using a variety of known properties of the human visual system. Under the assumption that human visual perception is highly adapted for extracting structural information from a scene, we introduce an alternative complementary framework for quality assessment based on the degradation of structural information. As a specific example of this concept, we develop a Structural Similarity Index and demonstrate its promise through a set of intuitive examples, as well as comparison to both subjective ratings and state-of-the-art objective methods on a database of images compressed with JPEG and JPEG2000.
          Article 0 comments Cited 388 times – based on 0 reviews     Review now
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            Image denoising using scale mixtures of Gaussians in the wavelet domain.

            J Portilla, V Strela, M.J. Wainwright … (2003)
            We describe a method for removing noise from digital images, based on a statistical model of the coefficients of an overcomplete multiscale oriented basis. Neighborhoods of coefficients at adjacent positions and scales are modeled as the product of two independent random variables: a Gaussian vector and a hidden positive scalar multiplier. The latter modulates the local variance of the coefficients in the neighborhood, and is thus able to account for the empirically observed correlation between the coefficient amplitudes. Under this model, the Bayesian least squares estimate of each coefficient reduces to a weighted average of the local linear estimates over all possible values of the hidden multiplier variable. We demonstrate through simulations with images contaminated by additive white Gaussian noise that the performance of this method substantially surpasses that of previously published methods, both visually and in terms of mean squared error.
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              Practical Poissonian-Gaussian noise modeling and fitting for single-image raw-data.

              M Trimeche, Alessandro Foi, Karen Egiazarian … (2008)
              We present a simple and usable noise model for the raw-data of digital imaging sensors. This signal-dependent noise model, which gives the pointwise standard-deviation of the noise as a function of the expectation of the pixel raw-data output, is composed of a Poissonian part, modeling the photon sensing, and Gaussian part, for the remaining stationary disturbances in the output data. We further explicitly take into account the clipping of the data (over- and under-exposure), faithfully reproducing the nonlinear response of the sensor. We propose an algorithm for the fully automatic estimation of the model parameters given a single noisy image. Experiments with synthetic images and with real raw-data from various sensors prove the practical applicability of the method and the accuracy of the proposed model.
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                Author and article information

                Journal
                Journal ID (nlm-ta): Sensors (Basel)
                Journal ID (iso-abbrev): Sensors (Basel)
                Title: Sensors (Basel, Switzerland)
                Publisher: Molecular Diversity Preservation International (MDPI)
                ISSN (Electronic): 1424-8220
                Publication date Collection: 2009
                Publication date (Electronic): 10 March 2009
                Volume: 9
                Issue: 3
                Pages: 1692-1713
                Affiliations
                [1 ] STMicroelectronics, Stradale Primosole 50, 95121 Catania, Italy; E-Mail: arcangelo.bruna@ 123456st.com
                [2 ] Università di Catania, Dipartimento di Matematica ed Informatica, Viale A. Doria 6, 95125 Catania, Italy; E-Mails: battiato@ 123456dmi.unict.it ; rosetta.rizzo@ 123456dmi.unict.it
                Author notes
                [* ]Author to whom correspondence should be addressed; E-Mail: angelo.bosco@ 123456st.com
                Article
                Publisher ID: sensors-09-01692
                DOI: 10.3390/s90301692
                PMC ID: 3345860
                PubMed ID: 22573981
                SO-VID: 10e90afd-c42f-4a58-9957-84eccc4150c1
                Copyright © © 2009 by the authors; licensee MDPI, Basel, Switzerland
                License:

                This article is an open-access article distributed under the terms and conditions of the Creative Commons Attribution license ( http://creativecommons.org/licenses/by/3.0/).

                History
                Date received : 18 November 2008
                Date revision received : 4 March 2009
                Date accepted : 9 March 2009
                Categories
                Subject: Article

                ScienceOpen disciplines: Biomedical engineering
                Keywords: hvs,noise reduction,color filter array,texture detection
                Data availability:
                ScienceOpen disciplines: Biomedical engineering
                Keywords: hvs, noise reduction, color filter array, texture detection

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