16 citations · 34 across the 7 of their papers we have counts for
7 papers
How to warm-start your unfolding network
Vicky Kouni
We present a new ensemble framework for boosting the performance of overparameterized unfolding networks solving the compressed sensing problem. We combine a state-of-the-art overp…
Generalization analysis of an unfolding network for analysis-based Compressed Sensing
Vicky Kouni, Yannis Panagakis
Unfolding networks have shown promising results in the Compressed Sensing (CS) field. Yet, the investigation of their generalization ability is still in its infancy. In this paper,…
DECONET: an Unfolding Network for Analysis-based Compressed Sensing with Generalization Error Bounds
Vicky Kouni, Yannis Panagakis
We present a new deep unfolding network for analysis-sparsity-based Compressed Sensing. The proposed network coined Decoding Network (DECONET) jointly learns a decoder that reconst…
Spark Deficient Gabor Frames for Inverse Problems
Vasiliki Kouni, Holger Rauhut
In this paper, we apply star-Digital Gabor Transform in analysis Compressed Sensing and speech denoising. Based on assumptions on the ambient dimension, we produce a window vector…
ADMM-DAD net: a deep unfolding network for analysis compressed sensing
Vasiliki Kouni, Georgios Paraskevopoulos, Holger Rauhut +1
In this paper, we propose a new deep unfolding neural network based on the ADMM algorithm for analysis Compressed Sensing. The proposed network jointly learns a redundant analysis…
Star DGT: a Robust Gabor Transform for Speech Denoising
Vasiliki Kouni, Holger Rauhut, Theoharis Theoharis
In this paper, we address the speech denoising problem, where Gaussian, pink and blue additive noises are to be removed from a given speech signal. Our approach is based on a redun…
Spark Deficient Gabor Frame Provides a Novel Analysis Operator for Compressed Sensing
Vasiliki Kouni, Holger Rauhut
The analysis sparsity model is a very effective approach in modern Compressed Sensing applications. Specifically, redundant analysis operators can lead to fewer measurements needed…