Estimating image blur in the wavelet domain

Rooms, Filip

doi:10.1109/icassp.2002.5745673

Cited by 25 publications

(14 citation statements)

References 9 publications

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“…To avoid this, preprocessing the image with a blur detection method is a must. Some methods are based on edge information (Chung et al, 2004;Marziliano et al, 2002;Rooms et al, 2002;Tong et al, 2004) or frequency domain characteristics (Aizenberg et al, 2002;n.d. ;2006;.…”

Section: Blur Detection and Identificationmentioning

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: Blur Detection and Identificationmentioning

confidence: 99%

Harnessing the Potentials of Image Extrema for Blind Restoration

Chong¹,

Tanaka²

2012

Image Restoration - Recent Advances and Applications

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“…Rooms et al 33 estimated image blur by analyzing the sharpness of its sharpest edges through evaluation of the Lipschitz exponent based on the analysis of the scalogram obtained by continuous wavelet transform. Onural and Kocatepe 34 proposed methods for hologram reconstruction and space-depth analysis for the three-dimensional determination of particle location by use of scaling-chirp functions.…”

Section: B Related Workmentioning

confidence: 99%

Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion

2004

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We propose a robust autofocus method for reconstructing digital Fresnel holograms. The numerical reconstruction involves simulating the propagation of a complex wave front to the appropriate distance. Since the latter value is difficult to determine manually, it is desirable to rely on an automatic procedure for finding the optimal distance to achieve high-quality reconstructions. Our algorithm maximizes a sharpness metric related to the sparsity of the signal's expansion in distance-dependent waveletlike Fresnelet bases. We show results from simulations and experimental situations that confirm its applicability.

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“…The latter ones include [2], [3], [5], [6], [10], [11]. The method proposed in this study is focused on estimating the level of blurriness in the image rather than estimating the PSF of the imaging system.…”

Section: Introductionmentioning

confidence: 99%

“…For example, Rooms et al [6] and later Tong et al [7] use the ability of wavelet transform to detect location of the edges. Marziliano et al [8], and more recently Wang et al [9] make use of rather simple techniques of Canny or Sobel filtering to determine the edge positions.…”

Section: Introductionmentioning

confidence: 99%

Image blur estimation based on the average cone of ratio in the wavelet domain

et al. 2009

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In this paper, we propose a new algorithm for objective blur estimation using wavelet decomposition. The central idea of our method is to estimate blur as a function of the center of gravity of the average cone ratio (ACR) histogram. The key properties of ACR are twofold: it is powerful in estimating local edge regularity, and it is nearly insensitive to noise. We use these properties to estimate the blurriness of the image, irrespective of the level of noise. In particular, the center of gravity of the ACR histogram is a blur metric. The method is applicable both in case where the reference image is available and when there is no reference. The results demonstrate a consistent performance of the proposed metric for a wide class of natural images and in a wide range of out of focus blurriness. Moreover, the proposed method shows a remarkable insensitivity to noise compared to other wavelet domain methods.

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Estimating image blur in the wavelet domain

Cited by 25 publications

References 9 publications

Harnessing the Potentials of Image Extrema for Blind Restoration

Harnessing the Potentials of Image Extrema for Blind Restoration

Autofocus for digital Fresnel holograms by use of a Fresnelet-sparsity criterion

Image blur estimation based on the average cone of ratio in the wavelet domain

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