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      Mean squared error: Love it or leave it? A new look at Signal Fidelity Measures

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          A universal image quality index

          Zhou Wang, A.C. Bovik (2002)
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            A new, fast, and efficient image codec based on set partitioning in hierarchical trees

            A. Said, W.A. Pearlman (1996)
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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.
              Article 0 comments Cited 274 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Title: IEEE Signal Processing Magazine
                Abbreviated Title: IEEE Signal Process. Mag.
                Publisher: Institute of Electrical and Electronics Engineers (IEEE)
                ISSN (Print): 1053-5888
                Publication date Created: January 2009
                Publication date (Print): January 2009
                Volume: 26
                Issue: 1
                Pages: 98-117
                Article
                DOI: 10.1109/MSP.2008.930649
                SO-VID: b6b67ac4-2512-489c-ad7e-0c77acebcc80
                Copyright © © 2009
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