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Structured Sparse Coding for Image Denoising or Pattern Detection

Publications associées (55)

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Learning from sparse codes

Pascal Frossard, Sofia Karygianni

In this paper we address the problem of learning image structures directly from sparse codes. We first model images as linear combinations of molecules, which are themselves groups of atoms from a redundant dictionary. We then formulate a new structure lea ...

Ieee2016

200 km Fiber-Loop Conventional Brillouin Distributed Sensor with 2m Spatial Resolution Using Image Denoising

Luc Thévenaz, Marcelo Alfonso Soto Hernandez

Non-local means image denoising method is optimized to boost the performance of Brillouin distributed fiber sensors. Results demonstrate ultra-long sensing over a 200-km fiber-loop with 2m resolution using a sensing scheme without any hardware sophisticati ...

OSA2016

Signal structure: from manifolds to molecules and structured sparsity

Sofia Karygianni

Effective representation methods and proper signal priors are crucial in most signal processing applications. In this thesis we focus on different structured models and we design appropriate schemes that allow the discovery of low dimensional latent struct ...

EPFL2015

Path-space Motion Estimation and Decomposition for Robust Animation Filtering

Wenzel Alban Jakob

Renderings of animation sequences with physics-based Monte Carlo light transport simulations are exceedingly costly to generate frame-by-frame, yet much of this computation is highly redundant due to the strong coherence in space, time and among samples. A ...

Eurographics Association2015

Compressed Sensing of Memoryless Sources

Saeid Haghighatshoar

Compressed sensing is a new trend in signal processing for efficient sampling and signal acquisition. The idea is that most real-world signals have a sparse representation in an appropriate basis and this can be exploited to capture the sparse signal by ta ...

EPFL2014

Metric Learning with Rank and Sparsity Constraints

Volkan Cevher, Baran Gözcü, Bubacarr Bah

Choosing a distance preserving measure or metric is fun- damental to many signal processing algorithms, such as k- means, nearest neighbor searches, hashing, and compressive sensing. In virtually all these applications, the efficiency of the signal process ...

Ieee2014

A survey of uncertainty principles and some signal processing applications

Benjamin Ricaud

The goal of this paper is to review the main trends in the domain of uncertainty principles and localization, highlight their mutual connections and investigate practical consequences. The discussion is strongly oriented towards, and motivated by signal pr ...

Springer2014

Sparse Approximation Using M-Term Pursuit and Application in Image and Video Coding

Pascal Frossard, Pierre Vandergheynst, Adel Rahmoune

This paper introduces a novel algorithm for sparse approximation in redundant dictionaries called the M-term pursuit (MTP). This algorithm decomposes a signal into a linear combination of atoms that are selected in order to represent the main signal compon ...

Institute of Electrical and Electronics Engineers2012

Filtered Variation method for denoising and sparse signal processing

Volkan Cevher

We propose a new framework, called Filtered Variation (FV), for denoising and sparse signal processing applications. These problems are inherently ill-posed. Hence, we provide regularization to overcome this challenge by using discrete time filters that ar ...

2012

Compressive Sampling Strategies for Multichannel Signals

Mohammad Golbabaee

Over the past decade researches in applied mathematics, signal processing and communications have introduced compressive sampling (CS) as an alternative to the Shannon sampling theorem. The two key observations making CS theory widely applicable to numerou ...

EPFL2012