Bandelet Image Approximation and Compression

Pennec, Erwan Le; Mallat, Stéphane

doi:10.1137/040619454

Cited by 171 publications

(72 citation statements)

References 16 publications

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“…To overcome this limitation Le Pennec and Mallat et al [42,44] proposed the Geometric regularity in an anisotropic way by eliminating the redundancy of wavelet transform using the concept of bandeletization. Bandelet transform is a major self adaptive multiscale geometry analysis method which exploits the recognized geometric information of images as compared to the non adaptive algorithms such as curvelet [9,33] and Contourlet transforms [7].…”

Section: Bandelet Transformmentioning

confidence: 99%

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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One of the major requirements of content based image retrieval (CBIR) systems is to ensure meaningful image retrieval against query images. The performance of these systems is severely degraded by the inclusion of image content which does not contain the objects of interest in an image during the image representation phase. Segmentation of the images is considered as a solution but there is no technique that can guarantee the object extraction in a robust way. Another limitation of the segmentation is that most of the image segmentation techniques are slow and their results are not reliable. To overcome these problems, a bandelet transform based image representation technique is presented in this paper, which reliably returns the information about the major objects found in an image. For image retrieval purposes, artificial neural networks (ANN) are applied and the performance of the system and achievement is evaluated on three standard data sets used in the domain of CBIR.

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Section: Bandelet Transformmentioning

confidence: 99%

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Ashraf

Bashir

Irtaza

et al. 2015

Entropy

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“…They are generally used for "cartoon-like" images, assumed to be piecewise smooth having smooth boundaries (Elad et al, 2005, Buades et al, 2010. (2) Tunable dictionaries, in which a basis or frame is generated under the control of particular parameter (discrete or continuous): wavelet packets (Meyer et al, 2000) (parameter is time-frequency subdivision) or bandelettes (Pennec and Mallat, 2005)(parameter is spatial position).…”

Section: Compressive Sensingmentioning

confidence: 99%

Super-Resolution of Hyperspectral Images Using Compressive Sensing Based Approach

Patel

Joshi

2012

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Over the past decade hyper spectral (HS) image analysis has turned into one of the most powerful and growing technologies in the field of remote sensing. While HS images cover large area at fine spectral resolution, their spatial resolutions are often too coarse for the use in various applications. Hence improving their resolution has a high payoff. This paper presents a novel approach for super-resolution (SR) of HS images using compressive sensing (CS). Besides ill-posedness of SR problem, the main challenge in HS super-resolution is to preserve spectral contents among all bands while increasing their spatial resolutions. In this work, we first obtain an initial estimate of the super-resolution on a reduced dimension HS data. The HS observations of different wavelengths are represented as linear combination of smaller number of basis image planes (BIPs) using principal component analysis (PCA). The novelty of our approach lies in using CS based approach to super-resolve the most informative PCA transformed image representing highest spectral variance (i.e. the first principal component). Our approach uses low and high spatial resolution dictionaries of patches generated by random sampling of raw patches of PCA transformed images that are generated using the training images having similar statistical properties. Using the sparsity constraint, low resolution test patch is represented as a sparse linear combination of relevant dictionary elements adaptively, that leads to initial estimate of super-resolved PCA image having maximum spectral variability. Since SR is an ill-posed problem, we obtain the final solution using a regularization framework considering the sparse coefficients obtained by the CS approach and the autoregressive (AR) parameters obtained from the initial estimate. The remaining PCA images are up-scaled using regularization, considering the same AR parameters which were obtained from super-resolved PCA image having maximum spectral variability. Experiments are conducted on real HS images collected by Airborne Visible/Infrared Imaging Spectrometer (AVIRIS). Visual inspections and quantitative comparison confirm that our method enhances spatial information without introducing significant spectral distortion.

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“…First generation of bandelet transform uses the vector field (Le Pennec & Mallat, 2001), which determines image regularities and irregularities. Therefore bandelet coefficients represent geometric flow defined by polynomial function.…”

Section: Bandeletsmentioning

confidence: 99%

Information Extraction and Despeckling of SAR Images with Second Generation of Wavelet Transform

Kseneman¹,

Gleich²

2012

Advances in Wavelet Theory and Their Applications in Engineering, Physics and Technology

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Bandelet Image Approximation and Compression

Cited by 171 publications

References 16 publications

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Content Based Image Retrieval Using Embedded Neural Networks with Bandletized Regions

Super-Resolution of Hyperspectral Images Using Compressive Sensing Based Approach

Information Extraction and Despeckling of SAR Images with Second Generation of Wavelet Transform

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