Browsing by Author "Yorulmaz, O."
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Item Open Access Deconvolution using Fourier transform phase, ℓ1 and ℓ2 balls, and filtered variation(Elsevier, 2018) Yorulmaz, O.; Çetin, A. EnisIn this article, we present a deconvolution software based on convex sets constructed from the phase of the Fourier Transform, bounded ℓ2 energy and ℓ1 energy of a given image. The iterative deconvolution algorithm is based on the method of projections onto convex sets. Another feature of the method is that it can incorporate an approximate total variation bound called filtered variation bound on the iterative deconvolution algorithm. The main purpose of this article is to introduce the open source software called projDeconv v2.Item Open Access Deconvolution using Fourier Transform phase, ℓ1 and ℓ2 balls, and filtered variation(Elsevier B.V., 2018) Yorulmaz, O.; Çetin, A. EnisIn this article, we present a deconvolution software based on convex sets constructed from the phase of the Fourier Transform, bounded ℓ2 energy and ℓ1 energy of a given image. The iterative deconvolution algorithm is based on the method of projections onto convex sets. Another feature of the method is that it can incorporate an approximate total variation bound called filtered variation bound on the iterative deconvolution algorithm. The main purpose of this article is to introduce the open source software called projDeconv v2.Item Open Access Detection of fungal damaged popcorn using image property covariance features(Elsevier, 2012) Yorulmaz, O.; Pearson, T. C.; Çetin, A.Covariance-matrix-based features were applied to the detection of popcorn infected by a fungus that causes a symptom called " blue-eye" . This infection of popcorn kernels causes economic losses due to the kernels' poor appearance and the frequently disagreeable flavor of the popped kernels. Images of kernels were obtained to distinguish damaged from undamaged kernels using image-processing techniques. Features for distinguishing blue-eye-damaged from undamaged popcorn kernel images were extracted from covariance matrices computed using various image pixel properties. The covariance matrices were formed using different property vectors that consisted of the image coordinate values, their intensity values and the first and second derivatives of the vertical and horizontal directions of different color channels. Support Vector Machines (SVM) were used for classification purposes. An overall recognition rate of 96.5% was achieved using these covariance based features. Relatively low false positive values of 2.4% were obtained which is important to reduce economic loss due to healthy kernels being discarded as fungal damaged. The image processing method is not computationally expensive so that it could be implemented in real-time sorting systems to separate damaged popcorn or other grains that have textural differences.Item Open Access Phase and TV based convex sets for blind deconvolution of microscopic images(Institute of Electrical and Electronics Engineers Inc., 2016) Tofighi M.; Yorulmaz, O.; Köse K.; Yıldırım, D. C.; Çetin-Atalay R.; Çetin, A. EnisIn this paper, two closed and convex sets for blind deconvolution problem are proposed. Most blurring functions in microscopy are symmetric with respect to the origin. Therefore, they do not modify the phase of the Fourier transform (FT) of the original image. As a result blurred image and the original image have the same FT phase. Therefore, the set of images with a prescribed FT phase can be used as a constraint set in blind deconvolution problems. Another convex set that can be used during the image reconstruction process is the Epigraph Set of Total Variation (ESTV) function. This set does not need a prescribed upper bound on the Total Variation (TV) of the image. The upper bound is automatically adjusted according to the current image of the restoration process. Both the TV of the image and the blurring filter are regularized using the ESTV set. Both the phase information set and the ESTV are closed and convex sets. Therefore they can be used as a part of any blind deconvolution algorithm. Simulation examples are presented.