Multichannel blind iterative image restoration

Šroubek, Filip; Flusser, Jan

doi:10.1109/tip.2003.815260

Cited by 154 publications

(70 citation statements)

References 33 publications

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“…However, these are usually computationally expensive and time consuming. To make the selection process less complicated, we will follow the concept used in (Sroubek & Flusser, 2003;You & Kaveh, 1996). The idea is based on the fact that the partial derivatives of the cost function are zero assuming that we are given the correct values of f and h. Thus equations 29 and 31 will become:…”

Section: Regularization Parametersmentioning

confidence: 99%

Harnessing the Potentials of Image Extrema for Blind Restoration

Chong¹,

Tanaka²

2012

Image Restoration - Recent Advances and Applications

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Section: Regularization Parametersmentioning

confidence: 99%

Harnessing the Potentials of Image Extrema for Blind Restoration

Chong¹,

Tanaka²

2012

Image Restoration - Recent Advances and Applications

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“…Most currently used denoising methods are based on anisotropic diffusion (Tschumperlé and Deriche, 2005;Sroubek and Flusser, 2003;Hamza et al, 2002) or wavelet thresholding techniques (Donoho, 1995;Coifman and Donoho, 1995;Portilla et al, 2003). Wavelet or multiresolution image denoising applications usually proceed in three stages: first a transformation, then a thresholding operation and finally the inverse transform for reconstructing the image.…”

Section: Application To Image Denoisingmentioning

confidence: 99%

Self-Invertible 2D Log-Gabor Wavelets

et al. 2007

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Abstract. Orthogonal and biorthogonal wavelets became very popular image processing tools but exhibit major drawbacks, namely a poor resolution in orientation and the lack of translation invariance due to aliasing between subbands. Alternative multiresolution transforms which specifically solve these drawbacks have been proposed. These transforms are generally overcomplete and consequently offer large degrees of freedom in their design. At the same time their optimization gets a challenging task. We propose here the construction of log-Gabor wavelet transforms which allow exact reconstruction and strengthen the excellent mathematical properties of the Gabor filters. Two major improvements on the previous Gabor wavelet schemes are proposed: first the highest frequency bands are covered by narrowly localized oriented filters. Secondly, the set of filters cover uniformly the Fourier domain including the highest and lowest frequencies and thus exact reconstruction is achieved using the same filters in both the direct and the inverse transforms (which means that the transform is self-invertible). The present transform not only achieves important mathematical properties, it also follows as much as possible the knowledge on the receptive field properties of the simple cells of the Primary Visual Cortex (V1) and on the statistics of natural images. Compared to the state of the art, the log-Gabor wavelets show excellent ability to segregate the image information (e.g. the contrast edges) from spatially incoherent Gaussian noise by hard thresholding, and then to represent image features through a reduced set of large magnitude coefficients. Such characteristics make the transform a promising tool for processing natural images.

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“…In algorithm development, a priori information is used in defining constraints on the solution and in defining a criterion or a quantitative description of the solution. The blind and non-blind deconvolutions were extensively studied, and many techniques were proposed for their solution (Kundur & Hatzinakos, 1996;Bertero & Boccacci, 1998;Biemond et al, 1990;Sroubek & Flusser, 2003). They usually involve some regularization which assures various statistical properties of the image or constrains on the estimated image and restoration filter according to some assumptions.…”

Section: Introductionmentioning

confidence: 99%