Τμήμα Φυσικής (Δημοσ. Π.Π. σε συνέδρια)
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Item Open Access University museums as mediators between university and society : the example of Patras University Science and Technology Museum(National and Kapodistrian University of Athens Press) Theologi-Gouti, Penelope; Vitoratos, Evangelos; Θεολόγη-Γκούτη, Πηνελόπη; Βιτωράτος, ΕυάγγελοςThis paper highlights the achievements of the Science and Technology Museum in order to fulfill its core goal, the connection of the university and society. The Museum's challenge is now to open new paths to the development perspective of the museum that will reinforce the museum and will give a central role to the academic heritage within the university society.Item Open Access Multi-Frame Super-Resolution Image Reconstruction Employing the Novel Estimator L1inv-Norm(2012-09-17) Panagiotopoulou, Antigoni; Παναγιωτοπούλου, ΑντιγόνηIn multi-frame Super-Resolution (SR) image reconstruction a single High-Resolution (HR) image is created from a sequence of Low-Resolution (LR) frames. This work considers stochastic regularized multi-frame SR image reconstruction from the data-fidelity point of view. In fact, a novel estimator named L1inv-norm is proposed for assuring fidelity to the measured data. This estimator presents the hybrid form of both L1 error norm and logarithm ln. The introduced L1inv-norm is combined with the Bilateral Total Variation (BTV) regularization. The proposed SR method is directly compared with an existing SR method which employs the Lorentzian estimator in combination with the BTV regularizer. The experimental results prove that the proposed technique predominates over the existing technique.Item Open Access Super-resolution reconstruction of thermal infrared images(2011-12-08) Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςIn this paper a high-resolution (HR) thermal infrared image is reconstructed from a sequence of subpixel shifted, aliased low-resolution (LR) frames, by means of a stochastic regularized super-resolution (SR) method. The Huber (H) cost function is employed to measure the difference between the projected estimate of the HR image and each LR frame. The bilateral Total Variation (TV) regularization is incorporated as a priori knowledge about the solution. The proposed HTV super-resolution approach that employs the Huber norm in combination with the bilateral TV regularization exhibits superior performance to former SR method. Thus the effect of outliers is significantly reduced and the high-frequency edge structures of the reconstructed HR thermal infrared image are preserved. The proposed technique is also tested on frames that are corrupted by Gaussian noise and proves superior when compared to existing regularized SR method.Item Open Access Super-resolution image reconstruction employing Kriging interpolation technique(2011-12-08) Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςIn this paper a high-resolution (HR) image is reconstructed from a sequence of subpixel shifted, aliased low-resolution (LR) frames by means of a novel nonuniform interpolation super-resolution (SR) method. A gradient-based algorithm estimates the horizontal and vertical shifts for each frame. Then, the uniformly spaced sampling points of the HR image are produced by means of Kriging interpolation. Wiener filtering is employed to deal with the restoration problem. The novelty of the proposed nonuniform interpolation approach to SR image reconstruction lies in the employment of Kriging interpolation technique. Comparisons with the original image demonstrate the superiority of our method to a conventional nonuniform interpolation one of SR image reconstruction.Item Open Access Interpolation in multispectral data using neural networks(2011-12-08) Tsagaris, Vassilis; Panagiotopoulou, Antigoni; Anastassopoulos, Vassilis; Τσαγκάρης, Βασίλειος; Παναγιωτοπούλου, Αντιγόνη; Αναστασόπουλος, ΒασίλειοςA novel procedure which aims in increasing the spatial resolution of multispectral data and simultaneously creates a high quality RGB fused representation is proposed in this paper. For this purpose, neural networks are employed and a successive training procedure is applied in order to incorporate in the network structure knowledge about recovering lost frequencies and thus giving fine resolution output color images. MERIS multispectral data are employed to demonstrate the performance of the proposed method.