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Towards super resolution in the compressed domain of learning-based image codecs

Related publications (37)

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Artificial neural networks in spectrum fatigue life prediction of composite materials

Anastasios Vassilopoulos

An artificial neural network has been proved to be a sufficient tool for modelling fatigue life of multidirectional composite laminates made of GFRP composite materials and tested under constant amplitude loading patterns. Modelling efficiency of the netwo ...

Elsevier2007

Nonlinear approximation with redundant multi-component dictionaries

Lorenzo Granai

The problem of efficiently representing and approximating digital data is an open challenge and it is of paramount importance for many applications. This dissertation focuses on the approximation of natural signals as an organized combination of mutually c ...

EPFL2006

Directionlets

Vladan Velisavljevic

Efficient representation of geometrical information in images is very important in many image processing areas, including compression, denoising and feature extraction. However, the design of transforms that can capture these geometrical features and repre ...

EPFL2005

Image security and processing in the JPEG 2000 compressed domain

Raphaël Grosbois

Digital images are becoming increasingly successful thanks to the development and the facilitated access to systems permitting their generation (i.e. camera, scanner, imaging software, etc). A digital image basically corresponds to a 2D discrete set of reg ...

EPFL2003

Low rate and scalable image coding with redundant representations

Pascal Frossard, Pierre Vandergheynst

New breakthroughs in image coding possibly rely in signal decomposition through non-separable basis functions. The work proposed in this paper provides an adaptive way of representing images as a sum of two-dimensional features. It presents a low bit-rate ...

2003

Adaptive wavelet thresholding for image denoising and compression

Martin Vetterli

The first part of this paper proposes an adaptive, data-driven threshold for image denoising via wavelet soft-thresh- olding. The threshold is derived in a Bayesian framework, and the prior used on the wavelet coefficients is the generalized Gaussian distr ...

2000

Lossy compression and wavelet thresholding for image denoising

Martin Vetterli

In recent work, it was proposed to use lossy compression to remove noise from corrupted signals, based on the rationale that a reasonable compression method retains the dominant signal features more than the randomness of the noise. To further understand a ...

1998