Context adaptive image denoising through modeling of curvelet domain statistics

Tessens, Linda; Pižurica, Aleksandra; Alecu, Alin; Munteanu, Adrian; Philips, Wilfried

doi:10.1117/1.2987723

Cited by 30 publications

(14 citation statements)

References 33 publications

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“…Moreover, we examine numerous candidate neighbors to decide if they correlated and if correlated whether they are beneficial to include. This is somehow similar to the work in reference [15] but different with the others which use a heuristically decided neighborhood. As another difference, we don't intend here to statistically model wavelet coefficients.…”

Section: Introductionmentioning

confidence: 82%

“…Statistics of these recent transformations can be examined by extensions of our wavelet modeling taking into account their individual idiosyncrasies. Although, there are already a couple of works addressing coefficient joint statistics analysis from different points of view, such as those considering Curvelets [13][14][15] or Contourlets [16], our work would be different in terms of the goals and means. We will try to find relation maps usable in different correlation-based and graph-based estimation frameworks using a conclusively large real image set and cross examining each neighborhood position by its significance measure.…”

Section: Discussionmentioning

confidence: 97%

“…Example of such works are [13][14][15] in Curvelet domain, [16] for Contourlets and [17] for wavelets. However, our work has its own characteristics.…”

Section: Introductionmentioning

confidence: 99%

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Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

et al. 2011

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This paper presents an empirical study of joint wavelet statistics for textures and other imagery to find an efficient correlation neighborhood. Since there is an established realization that modeling wavelet and other x-let coefficient relationships is crucial to any successful transform domain algorithm (such as Hidden Markov Trees), new works have been devoted to examine these dependencies from different aspects and propose an appropriate model. Because the time and computation complexity involved both in analyzing non-linear dependencies and in solving dependent models may restrict us to consider only a very small subset of contributing neighbors we focus our attention on linear dependencies (correlations) while having a squint on non-linear relations too. In this process, we study a collection of 5000 real images to corroborate our statistical analysis of the joint coefficient behavior and try to find an efficient and at the same time frugal relation map through different statistical means. The statistical observations are then certified by a coefficient significance measure and the competitiveness of the map is substantiated by plugging it into two dependent denoising frameworks. Z. Azimifar ( ) · M. Amiri

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Section: Introductionmentioning

confidence: 82%

Section: Discussionmentioning

confidence: 97%

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Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

et al. 2011

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“…Therefore, several assumptions are adopted broadly. For example, we can use the statistical or structural information [5][6][7] of the input data to build up the relationship between the missing elements and the known ones. However, many methods focus on the local relationship only.…”

Section: Introductionmentioning

confidence: 99%

Sparse and Truncated Nuclear Norm Based Tensor Completion

Han

Leung

Huang

et al. 2016

Neural Process Lett

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One of the main difficulties in tensor completion is the calculation of the tensor rank. Recently a tensor nuclear norm, which is equal to the weighted sum of matrix nuclear norms of all unfoldings of the tensor, was proposed to address this issue. However, in this approach, all the singular values are minimized simultaneously. Hence the tensor rank may not be well approximated. In addition, many existing algorithms ignore the structural information of the tensor. This paper presents a tensor completion algorithm based on the proposed tensor truncated nuclear norm, which is superior to the traditional tensor nuclear norm. Furthermore, to maintain the structural information, a sparse regularization term, defined in the transform domain, is added into the objective function. Experimental results showed that our proposed algorithm outperforms several state-of-the-art tensor completion schemes.

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“…This method is able to suppress the pseudoGibbs and curvelet like artifacts in denoised image. A context adaptive denoising method based on curvelet coefficients statistics modeling is proposed in [13]. This method works by distinguishing two classes of curvelet coefficients, one which is free of significant noise and the other has noise components.…”

Section: Introductionmentioning

confidence: 99%

Denoising of Document Images using Discrete Curvelet Transform for OCR Applications

Patvardhan¹,

Verma²,

Lakshmi³

2012

IJCA

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In this paper, a denoising and binarization scheme of document images corrupted by white Gaussian noise and Impulse noise is presented using Curvelet Transform. The ability of sparse representation and edge preservation of Curvelet transform is utilized. Impulse noise gets added during document scanning or after binarization of scanned document images. White Gaussian noise corrupts the document images during transmission. The presence of either type of noise or a combination of them can severely degrade the performance of any OCR system. In the proposed denoising scheme, the curvelet transform is used with level dependent threshold calculated by modified sqtwolog method (universal threshold) at each scale with estimation of noise standard deviation. The noisy curvelet coefficients are thresholded by Hard Thresholding method. After curvelet based denoising, the image is binarized using global Otsu method and post processed to smoothen the text boundaries and remove isolated pixels for better OCR performance. The curvelet based scheme is compared with a wavelet transform based scheme and a modified wavelet based scheme with edge preservation. The results show that curvelet based scheme performs better in case of images containing Gaussian, Impulse and a combination of both the noises. General TermsDocument Image Processing, OCR

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Context adaptive image denoising through modeling of curvelet domain statistics

Cited by 30 publications

References 33 publications

Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

Empirical Study of Wavelet Domain Image Joint Statistics and Proposition of an Efficient Correlation Map

Sparse and Truncated Nuclear Norm Based Tensor Completion

Denoising of Document Images using Discrete Curvelet Transform for OCR Applications

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