Browsing by Author "Kizgut, Ersin"

Now showing 1 - 2 of 2

Citation Count: Ülkü, İ., Kizgut, E. (2018). Large-scale hyperspectral image compression via sparse representations based on online learning. International Journal Of Applied Mathematics And Computer Science, 28(1), 197-207. http://dx.doi.org/10.2478/amcs-2018-0015
Large-scale hyperspectral image compression via sparse representations based on online learning
(Univ Zielona Gora Press, 2018) Ülkü, İrem; Kizgut, Ersin; 17575
In this study, proximity based optimization algorithms are used for lossy compression of hyperspectral images that are inherently large scale. This is the first time that such proximity based optimization algorithms are implemented with an online dictionary learning method. Compression performances are compared with the one obtained by various sparse representation algorithms. As a result, proximity based optimization algorithms are listed among the three best ones in terms of compression performance values for all hyperspectral images. Additionally, the applicability of anomaly detection is tested on the reconstructed images.
Citation Count: Ulku, Irem; Kizgut, Ersin, "Lossy Compressive Sensing Based on Online Dictionary Learning", Computing and Informatics, Vol. 38, No. 1, pp. 151-172, (2019).
Lossy Compressive Sensing Based on Online Dictionary Learning
(Slovak Acad Sciences Inst Informatics, 2019) Ülkü, İrem; Kizgut, Ersin; 17575
In this paper, a lossy compression of hyperspectral images is realized by using a novel online dictionary learning method in which three dimensional datasets can be compressed. This online dictionary learning method and blind compressive sensing (BCS) algorithm are combined in a hybrid lossy compression framework for the first time in the literature. According to the experimental results, BCS algorithm has the best compression performance when the compression bit rate is higher than or equal to 0.5 bps. Apart from observing rate-distortion performance, anomaly detection performance is also tested on the reconstructed images to measure the information preservation performance.