Yazar "Erkan, Ugur" seçeneğine göre listele

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    A new classification method using soft decision-making based on an aggregation operator of fuzzy parameterized fuzzy soft matrices
    (Tubitak Scientific & Technological Research Council Turkey, 2022) Memis, Samet; Enginoglu, Serdar; Erkan, Ugur
    Recently, a precise and stable machine learning algorithm, i.e. eigenvalue classification method (EigenClass), has been developed by using the concept of generalised eigenvalues in contrast to common approaches, such as k-nearest neighbours, support vector machines, and decision trees. In this paper, we offer a new classification algorithm called fuzzy parameterized fuzzy soft aggregation classifier (FPFS-AC) to combine the modelling ability of soft decision-making (SDM) and classification success of generalised eigenvalues. FPFS-AC constructs a decision matrix by employing the similarity measures of fuzzy parameterized fuzzy soft matrices (fpfs-matrices) and a generalised eigenvalue-based similarity measure. Then, it applies an SDM method based on the aggregation operator of fpfs-matrices to a decision matrix and classifies the given test sample. Afterwards, we perform an experimental study using 15 UCI datasets to manifest the success of our approach and compare FPFS-AC with the well-known and state-of-the-art classifiers (kNN, SVM, fuzzy kNN, EigenClass, and BM-fuzzy kNN) in terms of accuracy, precision, recall, macro F-score, micro F-score, and running time. Moreover, we statistically analyse the experimentally obtained data. Experimental and statistical results show that FPFS-AC outperforms the state-of-the-art classifiers in all the datasets concerning the five performance metrics.
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    A Recursive Mean Filter for Image Denoising
    (IEEE, 2019) Erkan, Ugur; Enginoglu, Serdar; Thanh, Dang N. H.
    In this article, we propose a Recursive Mean Filter (RMF) to remove the salt-and-pepper noise (SPN). We compare RMF with similar methods such as Decision Based Algorithm (DBA), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). In the experiments, we implement the test on 15 native images of the MATLAB library. To assess the denoising quality, we consider the following noise densities (noise levels): 10%, 30%, 50%, 70%, 90%; and use the well-known two quality metrics: Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that RMF removes the noise effectively and competes with other state-of-the-art denoising methods.
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    A Recursive mean filter for image denoising
    (Institute of Electrical and Electronics Engineers Inc., 2019) Erkan, Ugur; Enginoglu, Serdar; Thanh, Dang N.H.
    In this article, we propose a Recursive Mean Filter (RMF) to remove the salt-And-pepper noise (SPN). We compare RMF with similar methods such as Decision Based Algorithm (DBA), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF), and Noise Adaptive Fuzzy Switching Median Filter (NAFSMF). In the experiments, we implement the test on 15 native images of the MATLAB library. To assess the denoising quality, we consider the following noise densities (noise levels): 10%, 30%, 50%, 70%, 90%; and use the well-known two quality metrics: Peak Signal-To-Noise Ratio (PSNR) and Structural Similarity (SSIM). The results show that RMF removes the noise effectively and competes with other state-of-The-Art denoising methods. © 2019 IEEE.
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    Adaptive frequency median filter for the salt and pepper denoising problem
    (Inst Engineering Technology-Iet, 2020) Erkan, Ugur; Enginoglu, Serdar; Thanh, Dang N. H.; Le Minh Hieu
    In this article, the authors propose an adaptive frequency median filter (AFMF) to remove the salt and pepper noise. AFMF uses the same adaptive condition of adaptive median filter (AMF). However, AFMF employs frequency median to restore grey values of the corrupted pixels instead of the median of AMF. The frequency median can exclude noisy pixels from evaluating a grey value of the centre pixel of the considered window, and it focuses on the uniqueness of grey values. Hence, the frequency median produces a grey value closer to the original grey value than the one by the median of AMF. Therefore, AFMF outperforms AMF. In experiments, the authors tested the proposed method on a variety of natural images of the MATLAB library, as well as the TESTIMAGES data set. Additionally, they also compared the denoising results of AFMF to the ones of other state-of-the-art denoising methods. The results showed that AFMF denoises more effectively than other methods.
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    An image encryption scheme based on chaotic logarithmic map and key generation using deep CNN
    (Springer, 2022) Erkan, Ugur; Toktas, Abdurrahim; Enginoglu, Serdar; Akbacak, Enver; Thanh, Dang N. H.
    A secure and reliable image encryption scheme is presented, which depends on a novel chaotic log-map, deep convolution neural network (CNN) model, and bit reversion operation for the manipulation process. CNN is utilized to generate a public key to be based on the image in order to enhance the key sensitivity of the scheme. Initial values and control parameters are then obtained from the key to be used in the chaotic log-map, and thus a chaotic sequence is produced for the encrypting operations. The scheme then encrypts the images by scrambling and manipulating the pixels of images through four operations: permutation, DNA encoding, diffusion, and bit reversion. The encryption scheme is precisely examined for the well-known images in terms of various cryptanalyses such as key-space, key sensitivity, information entropy, histogram, correlation, differential attack, noisy attack, and cropping attack. To corroborate the image encryption scheme, the visual and numerical results are even compared with available scores of the state of the art. Therefore, the proposed log-map-based image encryption scheme is successfully verified and validated by superior absolute and comparative results. As future work, the proposed log-map can be extended to combinational multi-dimensional with existing efficient chaotic maps.
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    An Iterative Image Inpainting Method Based on Similarity of Pixels Values
    (IEEE, 2019) Erkan, Ugur; Enginoglu, Serdar; Thanh, Dang N. H.
    Image inpainting is a process of completion of missing places by using other undamaged sections of the image or removal of unwanted objects of the image. In this study, we propose a novel image inpainting method. This method constitutes an essential place in image processing. This proposed method fills the corrupted area by using the similarity of the boundary pixels values around that corrupted regions in every iteration step. Afterwards, to evaluate image inpainting quality of the proposed method, we use Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity (SSIM) metrics and present some visual results. The acquired results show that our proposed inpainting method gives an outstanding performance to fill the corrupted areas and to remove objects. We also discuss the need for further research.
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    An Iterative Mean Filter for Image Denoising
    (IEEE-Inst Electrical Electronics Engineers Inc, 2019) Erkan, Ugur; Dang Ngoc Hoang Thanh; Le Minh Hieu; Enginoglu, Serdar
    We propose an Iterative Mean Filter (IMF) to eliminate the salt-and-pepper noise. IMF uses the mean of gray values of noise-free pixels in a fixed-size window. Unlike other nonlinear filters, IMF does not enlarge the window size. A large size reduces the accuracy of noise removal. Therefore, IMF only uses a window with a size of 3 x 3. This feature is helpful for IMF to be able to more precisely evaluate a new gray value for the center pixel. To process high-density noise effectively, we propose an iterative procedure for IMF. In the experiments, we operationalize Peak Signal-to-Noise Ratio (PSNR), Visual Information Fidelity, Image Enhancement Factor, Structural Similarity (SSIM), and Multiscale Structure Similarity to assess image quality. Furthermore, we compare denoising results of IMF with ones of the other state-of-the-art methods. A comprehensive comparison of execution time is also provided. The qualitative results by PSNR and SSIM showed that IMF outperforms the other methods such as Based-on Pixel Density Filter (BPDF), Decision-Based Algorithm (DBA), Modified Decision-Based Untrimmed Median Filter (MDBUTMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Adaptive Type-2 Fuzzy Filter (FDS): for the IMAGESTEST dataset - BPDF (25.36/0.756), DBA (28.72/0.8426), MDBUTMF (25.93/0.8426), NAFSMF (29.32/0.8735), AWMF (32.25/0.9177), DAMF (31.65/0.9154), FDS (27.98/0.8338), and IMF (33.67/0.9252); and for the BSDS dataset - BPDF (24.95/0.7469), DBA (26.84/0.8061), MDBUTMF (26.25/0.7732), NAFSMF (27.26/0.8191), AWMF (28.89/0.8672), DAMF (29.11/0.8667), FDS (26.85/0.8095), and IMF (30.04/0.8753).
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    Different applied median filter in salt and pepper noise
    (Pergamon-Elsevier Science Ltd, 2018) Erkan, Ugur; Gokrem, Levent; Enginoglu, Serdar
    In this paper, we proposed a new method, Different Applied Median Filter (DAMF), to remove salt and pepper (SAP) noise at all densities. We then explained some basic notions of it. Afterwards, we compared the results of DAMF method and some other methods by using Peak Signal to Noise Ratio (PSNR) and Structural Similarity (SSIM) for some images such as Cameraman and Lena. For example, for Cameraman image with a SAP noise ratio of 30%, PSNR and SSIM results of PSMF, DBA, MDBUTMF and NAFSM methods are 28.27/29.28/29.44/32.09 and 0.9044/0.9324/0.7740/0.9494 respectively while PSNR and SSIM results of DAMF method are 36.83 and 0.9844, respectively. We finally showed that DAME could be successfully removed SAP noise at all densities. (C) 2018 Elsevier Ltd. All rights reserved.
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    Improved Adaptive Weighted Mean Filter for Salt-and-Pepper Noise Removal
    (Institute of Electrical and Electronics Engineers Inc., 2020) Erkan, Ugur; Thanh, Dang N. H.; Enginoglu, Serdar; Memis, Samet
    In this study, we propose an improved adaptive weighted mean filter (IAWMF) to remove salt-and-pepper noise. The most prominent advantage of IAWMF is its ability to take into account the weights of noise-free pixels in the adaptive window. Hence, the new grey value occurs closer to the original grey value of the centre pixel than the grey value computed by the adaptive weighted mean filter (AWMF). Moreover, the proposed method utilises the advantage of AWMF to reduce the error of detecting noisy pixels. In the experiments, we compare the denoising results of the proposed method with other state-of-the-art image denoising methods. The results confirm that IAWMF outperforms other methods. © 2020 IEEE.
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    Pixel similarity-based adaptive Riesz mean filter for salt-and-pepper noise removal
    (Springer, 2019) Enginoglu, Serdar; Erkan, Ugur; Memis, Samet
    In this study, we propose a new method, i.e. Adaptive Riesz Mean Filter (ARmF), by operationalizing pixel similarity for salt-and-pepper noise (SPN) removal. Afterwards, we compare the results of ARmF, A New Adaptive Weighted Mean Filter (AWMF), Different Applied Median Filter (DAMF), Noise Adaptive Fuzzy Switching Median Filter (NAFSMF), Based on Pixel Density Filter (BPDF), Modified Decision-Based Unsymmetric Trimmed Median Filter (MDBUTMF) and Decision-Based Algorithm (DBA) by using Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), Image Enhancement Factor (IEF), and Visual Information Fidelity (VIF) for 20 traditional test images (Lena, Cameraman, Barbara, Baboon, Peppers, Living Room, Lake, Plane, Hill, Pirate, Boat, House, Bridge, Elaine, Flintstones, Flower, Parrot, Dark-Haired Woman, Blonde Woman, and Einstein), 40 test images in the TESTIMAGES Database, and 200 RGB test images from the UC Berkeley Dataset ranging in noise density from 10% to 90%. Moreover, we compare the running time of these algorithms. These results show that ARmF outperforms the methods mentioned above. We finally discuss the need for further research.