A filter bank for the directional decomposition of images: theory and design

Bamberger, R.H.; Smith, M.J.T.

doi:10.1109/78.127960

Cited by 803 publications

(385 citation statements)

References 45 publications

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“…PDFB consist of a double filter bank structure for obtaining sparse expansions for typical images having smooth contours. In this double filter bank, the laplacian pyramid (LP) [16] is first used to capture the point discontinuities, and then followed by a directional filter bank (DFB) [15] to link point discontinuities into linear structure. contourlet decomposition is shown in Fig.…”

Section: Contourlet Transformmentioning

confidence: 99%

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

Ramezani¹,

Keynia²,

Ramezani³

2013

IJFCC

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Abstract-Steganography is the science that involves communicating secret data in an appropriate multimedia carrier. This paper presents the application of contourlet Transform and Genetic Algorithm (GA) in a novel steganography scheme. We employ a genetic algorithm based mapping function to embed data in Discrete contourlet Transform coefficients in 4x4blocks on the cover image. The optimal pixel adjustment process (OPAP) is applied after embedding the message. GA employed to obtain an optimal mapping function to lessen the error difference between the cover and the stego image and use the block mapping method to preserve the local image properties. Also we applied the OPAP to increase the hiding capacity of the algorithm in comparison with other systems.According to the experimental results, the proposed method is capable of providing a larger embedding capacity without causing noticeable distortions of stego-images in comparison with similar existing methods. Moreoverthe results of our experiments show that employing two of powerful steganalyzers on stego-images produced by our method, they could not discriminate between stego and clean-images reliably.Index Terms-Steganography, discrete contourlet transform, genetic algorithm, optimal pixel adjustment process.

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Section: Contourlet Transformmentioning

confidence: 99%

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

Ramezani¹,

Keynia²,

Ramezani³

2013

IJFCC

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“…The need for directional filtering in order to improve multidimensional data processing was early recognized, for example, in the works [1,23,41]. More recently, new Key words and phrases.…”

Section: Introductionmentioning

confidence: 99%

“…This framework is a generalization of the classical theory from which traditional wavelets are derived, and it provides a very flexible setting for the construction of many truly multidimensional variants of wavelets, such as the well known construction of shearlets 1 . Several additional sophisticated constructions using this approach were obtained by Blanchard [4,5], by Kryshtal and Blanchard in a paper which exploits the connection with crystallographic groups [6], and by Kryshtal et al [32].…”

Section: Introductionmentioning

confidence: 99%

Directional Multiscale Processing of Images Using Wavelets with Composite Dilations

2012

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“…quincunx), as proposed by Kovačević (1991), Kovačević and Vetterli (1992) and Cohen and Daubechies (1993), but these methods are computationally complex and require a challenging design of the associated 2-D filter banks. Furthermore, several general multi-dimensional multi-band filter design methods have been proposed by Bamberger and Smith (1992), Chen and Vaidyanathan (1993), Tay and Kingsbury (1993) and Phoong et al (1995) resulting in filters with separable polyphase components.…”

Section: Introductionmentioning

confidence: 99%

“…Apart from exploiting geometrical coherence, multi-directional (M-DIR) processing has also been applied to image denoising and classification. Examples of such transforms are the steerable pyramids (Simoncelli et al 1992), the cortex transform (Watson, 1987), complex wavelets (Kingsbury, 2001), the directional wavelet analysis (Zuidwijk, 2000), directional filter banks (Bamberger and Smith, 1992, Phoong et al, 1995, Rosiles and Smith, 2003, brushlets (Meyer and Coifman, 1997), and the associative representation of visual information (Granlund and Knutsson, 1990). Some other methods involve directionally adaptive processing in order to preserve edges in images (Muresan and Parks, 2000, Orchard, 2001, Hirakawa andParks, 2005), whereas the method proposed by Cunha et al (2006) imposes DVM in either critically sampled or oversampled filter banks.…”

Section: Introductionmentioning

confidence: 99%

Sparse Image Representation by Directionlets

Velisavljević¹,

Vetterli²,

Beferull-Lozano³

et al. 2010

Advances in Imaging and Electron Physics

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In spite of the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation is limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertical directions. One-dimensional (1-D) discontinuities in images (edges or contours) that are very important elements in visual perception, intersect too many wavelet basis functions and lead to a non-sparse representation. To capture efficiently these elongated structures characterized by geometrical regularity along different directions (not only the horizontal and vertical), a more complex multi-directional (M-DIR) and asymmetric transform is required. We present a lattice-based perfect reconstruction and critically sampled asymmetric M-DIR WT. The transform retains the separable filtering and subsampling and the simplicity of computations and filter design from the standard two-dimensional (2-D) WT, unlike what is the case for some other directional transform constructions (e.g. curvelets, contourlets or edgelets). The corresponding asymmetric basis functions, called directionlets, have directional vanishing moments (DVM) along any two directions with rational slopes, which allows for a sparser representation of elongated and oriented features. As a consequence of the improved sparsity, directionlets provide an efficient tool for non-linear approximation (NLA) of images, significantly outperforming the standard 2-D WT. Furthermore, directionlets combined with wavelet-based image compression methods lead to a gain in the performance in terms of both the mean-square error and visual quality, especially at the low bit-rate compression, while having the same complexity. Finally, a shift-invariant non-subsampled version of directionlets is successfully implemented in image interpolation, where critical sampling is not a key requirement.

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A filter bank for the directional decomposition of images: theory and design

Cited by 803 publications

References 45 publications

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

A Novel Image Steganography in Contourletdomain Using Genetic Algorithm

Directional Multiscale Processing of Images Using Wavelets with Composite Dilations

Sparse Image Representation by Directionlets

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