137 citations · 453 across the 19 of their papers we have counts for
6 papers · 1 filter
Preventing Overfitting in Deep Image Prior for Hyperspectral Image Denoising
Panagiotis Gkotsis, Athanasios A. Rontogiannis
Deep image prior (DIP) is an unsupervised deep learning framework that has been successfully applied to a variety of inverse imaging problems. However, DIP-based methods are inhere…
Low-rank and Sparse NMF for Joint Endmembers' Number Estimation and Blind Unmixing of Hyperspectral Images
Paris V. Giampouras, Athanasios A. Rontogiannis, Konstantinos D. Koutroumbas
Estimation of the number of endmembers existing in a scene constitutes a critical task in the hyperspectral unmixing process. The accuracy of this estimate plays a crucial role in…
Sparsity-aware Possibilistic Clustering Algorithms
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper two novel possibilistic clustering algorithms are presented, which utilize the concept of sparsity. The first one, called sparse possibilistic c-means, exploits spars…
On the convergence of the sparse possibilistic c-means algorithm
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper, a convergence proof for the recently proposed sparse possibilistic c-means (SPCM) algorithm is provided, utilizing the celebrated Zangwill convergence theorem. It is…
Simultaneously sparse and low-rank abundance matrix estimation for hyperspectral image unmixing
Paris Giampouras, Konstantinos Themelis, Athanasios Rontogiannis +1
In a plethora of applications dealing with inverse problems, e.g. in image processing, social networks, compressive sensing, biological data processing etc., the signal of interest…
A Novel Adaptive Possibilistic Clustering Algorithm
Spyridoula D. Xenaki, Konstantinos D. Koutroumbas, Athanasios A. Rontogiannis
In this paper a novel possibilistic c-means clustering algorithm, called Adaptive Possibilistic c-means, is presented. Its main feature is that {\it all} its parameters, after thei…