Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu
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Browsing Elektrik Elektronik Mühendisliği Bölümü Yayın Koleksiyonu by Author "17575"
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Article 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-2018-0015Large-scale hyperspectral image compression via sparse representations based on online learning(Univ Zielona Gora Press, 2018) Ülkü, İrem; Kizgut, Ersin; 17575In 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.Conference Object Citation Count: Ulku, Irem; Toreyin, B. Ugur, "Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding", International Workshop on Computational Intelligence for Multimedia Understanding (IWCIM), (2014).Lossy Compression of Hyperspectral Images Using Online Learning Based Sparse Coding(IEEE, 2014) Ülkü, İrem; Töreyin, Behçet Uğur; 17575A lossy hyperspectral image compression method is proposed using online learning based sparse coding. The least number of coefficients are obtained to represent hyperspectral images by applying the sparse coding algorithm which is based on a dicriminative online dictionary learning method. Results indicate that a pre-analysis of the number of non-zero dictionary elements may help in improving the overall compression quality.Article 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; 17575In 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.Article Citation Count: Ülkü, İ., Töreyin, B.U. (2015). Sparse coding of hyperspectral imagery using online learning. Signal Image And Video Processing, 9(4), 959-966. http://dx.doi.org/10.1007/s11760-015-0753-9Sparse coding of hyperspectral imagery using online learning(Springer, 2015) Ülkü, İrem; Töreyin, Behçet Uğur; 17575; 19325Sparse coding ensures to express the data in terms of a few nonzero dictionary elements. Since the data size is large for hyperspectral imagery, it is reasonable to use sparse coding for compression of hyperspectral images. In this paper, a hyperspectral image compression method is proposed using a discriminative online learning-based sparse coding algorithm. Compression and anomaly detection tests are performed on hyperspectral images from the AVIRIS dataset. Comparative rate-distortion analyses indicate that the proposed method is superior to the state-of-the-art hyperspectral compression techniques.Article Citation Count: Ülkü, İ., Töreyin, B.U. (2015). Sparse representations for online-learning-based hyperspectral image compression. Applied Optics, 54(29), 8625-8631. http://dx.doi.org/ 10.1364/AO.54.008625Sparse representations for online-learning-based hyperspectral image compression(Optical Soc Amer, 2015) Ülkü, İrem; Töreyin, Behçet Uğur; 17575; 19325Sparse models provide data representations in the fewest possible number of nonzero elements. This inherent characteristic enables sparse models to be utilized for data compression purposes. Hyperspectral data is large in size. In this paper, a framework for sparsity-based hyperspectral image compression methods using online learning is proposed. There are various sparse optimization models. A comparative analysis of sparse representations in terms of their hyperspectral image compression performance is presented. For this purpose, online-learning-based hyperspectral image compression methods are proposed using four different sparse representations. Results indicate that, independent of the sparsity models, online-learning-based hyperspectral data compression schemes yield the best compression performances for data rates of 0.1 and 0.3 bits per sample, compared to other state-of-the-art hyperspectral data compression techniques, in terms of image quality measured as average peak signal-to-noise ratio.
