Konstantina Fotiadou

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Enhancing the quality of low light images is a critical processing function both from an aesthetics and an information extraction point of view. This work proposes a novel approach for enhancing images captured under low illumination conditions based on the mathematical framework of Sparse Representations. In our model, we utilize the sparse representation(More)
Motivation. Multi and Hyperspectral remote sensing imagery provide valuable insights regarding the composition of a scene and significantly facilitate tasks like object and material recognition, spectral unmixing and region clustering, among others [1], [2]. However, current remote sensing imaging architectures are unable to concurrently acquire high(More)
The spectral dimension of hyperspectral imaging (HSI) systems plays a fundamental role in numerous terrestrial and earth observation applications, including spectral unmixing, target detection, and classification among others. However, in several cases the spectral resolution of HSI systems is sacrificed for the shake of spatial resolution, as such in the(More)
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