Directional Multiscale Processing of Images Using Wavelets with Composite Dilations

Easley, Glenn R.; Labate, Demetrio; Patel, Vishal M.

doi:10.1007/s10851-012-0385-4

Cited by 17 publications

(8 citation statements)

References 42 publications

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“…Shearlets form a Parseval frame (tight frame with bounds equal to 1) for L 2 (R 2 ) given the appropriate choice of the generating function ψ % [16]. An M-channel filter bank implementation can be done by using the techniques given in [17]. As a consequence, its implementation has a complexity of O(N 2 log2(N)) for an N × N image.…”

Section: Shearlet Transformmentioning

confidence: 99%

Robust local features for remote face recognition

Chen

Patel

Liu

et al. 2017

Image and Vision Computing

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In this paper, we propose a robust local descriptor for face recognition. It consists of two components, one based on a shearlet-decomposition and the other on local binary pattern (LBP). Shearlets can completely analyze the singular structures of piecewise smooth images, which is useful since singularities and irregular structures carry useful information in an underlying image. Furthermore, LBP is effective for describing the edges extracted by shearlets even when the images contain high level of noise. Experimental results using the Face Recognition Grand Challenge (FRGC) dataset show that the proposed local descriptor significantly outperforms many widely used features (e.g., Gabor and deep learning based features) when the images are corrupted by random noise, demonstrating the strong noise robustness of our approach. In addition, experimental results show promising results for two challenging datasets which have poor image quality, i.e., a remote face dataset and the Point and Shoot Face Recognition Challenge (PaSC) dataset.

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Section: Shearlet Transformmentioning

confidence: 99%

Robust local features for remote face recognition

Chen

Patel

Liu

et al. 2017

Image and Vision Computing

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“…We refer the reader to Ref. 16 for an extensive evaluation of the denoising performances of alternative composite wavelets such as the hyperbolic-based ones.…”

Section: Methodsmentioning

confidence: 99%

Multicomposite wavelet estimation

2011

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In this work, we present a new approach to image denoising derived from the general framework of wavelets with composite dilations. This framework extends the traditional wavelet approach by allowing for waveforms to be defined not only at various scales and locations but also according to various orthogonal transformations such as shearing transformations. The shearlet representation is, perhaps, the most widely known example of wavelets with composite dilations. However, many other representations are obtained within this framework, where directionality properties are controlled by different types of orthogonal matrices, such as the newly defined hyperbolets. In this paper, we show how to take advantage of different wavelets with composite dilations to sparsely represent important features such as edges and texture independently, and apply these techniques to derive improved algorithms for image denoising.

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“…Related to this, shearlets provide optimally sparse approximation properties for the class of cartoon-like images, a simplified model of natural images, outperforming conventional wavelets and other traditional methods (Guo and Labate, 2007). The properties of shearlets have been already successfully employed in a number of image processing application, including denoising, edge detection and feature extraction (Easley and Labate, 2012and Easley et al, 2014and Yi et al, 2009. It is therefore natural to take advantage of the properties of shearlets as part of our face recognition approach.…”

Section: Shearletmentioning

confidence: 99%

Sparse multi-stage regularized feature learning for robust face recognition

Borgi

Labate

Arbi

et al. 2015

Expert Systems with Applications

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Abstract-The major limitation in current facial recognition systems is that they do not perform very well in uncontrolled environments, that is, when faces present variations in pose, illumination, facial expressions and environment. This is a serious obstacle in applications such as law enforcement and surveillance systems. To address this limitation, in this paper we introduce an improved approach to ensure robust face recognition, that relies on two innovative ideas. First, we apply a new multiscale directional framework, called Shearlet Network (SN), to extract facial features. The advantage of this approach comes from the highly sparse representation properties of the shearlet framework that is especially designed to robustly extract the fundamental geometric content of an image. Second, we apply a refinement of the Multi-Task Sparse Learning (MTSL) framework to exploit the relationships among multiple shared tasks generated by changing the regularization parameter during the recognition stage. We provide extensive numerical tests to show that our Sparse Multi-Regularized Shearlet Network (SMRSN) algorithm performs very competitively when compared against different state-of-the-art methods on different experimental protocols, including face recognition in uncontrolled conditions and single-sample-per-person.

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Directional Multiscale Processing of Images Using Wavelets with Composite Dilations

Cited by 17 publications

References 42 publications

Robust local features for remote face recognition

Robust local features for remote face recognition

Multicomposite wavelet estimation

Sparse multi-stage regularized feature learning for robust face recognition

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