Publications related to Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Jean-Louis Scartezzini, Jérôme Henri Kämpf, Yujie Wu

The bidirectional transmittance distribution function (BTDF), which is used to characterize the light transmission of a complex fenestration system (CFS), commonly involves bulky volume of data that can be a challenge to data storage and transmission in li ...

2019

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Jean-Louis Scartezzini, Jérôme Henri Kämpf, Yujie Wu

The bidirectional transmittance distribution function (BTDF), which is used to characterize the light transmission of a complex fenestration system (CFS), commonly involves bulky volume of data that can be a challenge to data storage and transmission in li ...

2019

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Michaël Unser, Virginie Sophie Uhlmann, Pedram Pad

A crucial component of steerable wavelets is the radial profile of the generating function in the frequency domain. In this paper, we present an infinite-dimensional optimization scheme that helps us find the optimal profile for a given criterion over the ...

IEEE2016

Two-Dimensional Discrete Wavelet Transform on Large Images for Hybrid Computing Architectures: GPU and CELL

Pawel Lichocki

The Discrete Wavelet Transform (DWT) has gained the momentum in signal processing and image compression over the last decade bringing the concept up to the level of new image coding standard JPEG2000. Thanks to many added values in DWT, in particular inher ...

Springer Berlin Heidelberg2012

Optimally Localized Wavelets and Smoothing Kernels

Kunal Narayan Chaudhury

It is well-known that the Gaussian functions and, more generally, their modulations-translations (the Gabor functions) have the unique property of being optimally localized in space and frequency in the sense of Heisenberg's uncertainty principle. In this ...

EPFL2011

Sparse Image Representation by Directionlets

Martin Vetterli, Vladan Velisavljevic, Pier Luigi Dragotti

Despite the success of the standard wavelet transform (WT) in image processing in recent years, the efficiency and sparsity of its representation are limited by the spatial symmetry and separability of its basis functions built in the horizontal and vertic ...

Elsevier2010

Sensing and Recovery under Sparsity Constraints

Ali Hormati

Popular transforms, like the discrete cosine transform or the wavelet transform, owe their success to the fact that they promote sparsity. These transforms are capable of extracting the structure of a large class of signals and representing them by a few t ...

EPFL2010

Gabor Wavelet Analysis and the Fractional Hilbert Transform

Michaël Unser, Kunal Narayan Chaudhury

We propose an amplitude-phase representation of the dual-tree complex wavelet transform (DT-ℂWT) which provides an intuitive interpretation of the associated complex wavelet coefficients. The representation, in particular, is based on the shifting action o ...

SPIE2009

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions

Michaël Unser, Dimitri Nestor Alice Van De Ville, Ildar Khalidov

Tomographic reconstruction from positron emission tomography (PET) data is an ill-posed problem that requires regularization. An attractive approach is to impose an

\ell_{ 1 }

-regularization constraint, which favors sparse solutions in the wavelet dom ...

Institute of Electrical and Electronics Engineers2008

Space-frequency quantization for image compression with directionlets

Martin Vetterli, Vladan Velisavljevic

The standard separable 2-D wavelet transform (WT) has recently achieved a great success in image processing because it provides a sparse representation of smooth images. However, it fails to efficiently capture 1-D discontinuities, like edges or contours. ...

2007

Learning-Based Image Compression using Convolutional Autoencoder and Wavelet Decomposition

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Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Two-Dimensional Discrete Wavelet Transform on Large Images for Hybrid Computing Architectures: GPU and CELL

Optimally Localized Wavelets and Smoothing Kernels

Sparse Image Representation by Directionlets

Sensing and Recovery under Sparsity Constraints

Gabor Wavelet Analysis and the Fractional Hilbert Transform

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions

Space-frequency quantization for image compression with directionlets

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Performance assessment of the BTDF data compression based on wavelet transforms in daylighting simulation

Two-Dimensional Discrete Wavelet Transform on Large Images for Hybrid Computing Architectures: GPU and CELL

Optimally Localized Wavelets and Smoothing Kernels

Sparse Image Representation by Directionlets

Sensing and Recovery under Sparsity Constraints

Space-frequency quantization for image compression with directionlets

Gabor Wavelet Analysis and the Fractional Hilbert Transform

Maximally Localized Radial Profiles for Tight Steerable Wavelet Frames

Dynamic PET Reconstruction Using Wavelet Regularization with Adapted Basis Functions