Kaplan, Nur Hüseyin
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Kaplan, NH
Kaplan, N.H.
NH Kaplan
N.H. Kaplan
Nur Hüseyin Kaplan
Kaplan Nur Hüseyin
Kaplan, N.H.
NH Kaplan
N.H. Kaplan
Nur Hüseyin Kaplan
Kaplan Nur Hüseyin
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Prof. Dr.
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huseyin.kaplan@erzurum.edu.tr
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4.3. Elektrik Elektronik Mühendisliği Bölümü
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Scholarly Output
27
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16
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0
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3
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Competency Cloud
27 results
Scholarly Output Search Results
Now showing 1 - 10 of 27
Conference Object Remote Sensing Image Enhancement via Bilateral Filtering(Institute of Electrical and Electronics Engineers Inc., 2017) Kaplan, N.H.; Erer, I.; Gulmus, N.Remote sensing image enhancement methods have to increase the contrast and emphasize the edges, while preserving the color. In this study, an enhancement method based on bilateral filtering is proposed. We propose to extract the details of the image by a multiscale bilateral filter and add these details to the original image using a weighting scheme. Visual results and evaluation metrics show that the proposed method, enhance the image better than the former methods while it better preserves the original color information. © 2017 IEEE.Conference Object Pansharpening Via Weighted Additive Wavelet Transform(Institute of Electrical and Electronics Engineers Inc., 2016) Kaplan, N.H.; Erer, I.In additive wavelet transform (AWT) based classical pansharpening methods, the details of the panchromatic (PAN) image added to the multispectral (MS) image directly or by weighting after the detail extraction. In this paper, the details will be weighted during the detail extraction process. The details of the PAN image will be obtained by a weighted additive wavelet transform (WAWT). In order to achieve this aim, the range parameter of WAWT will be optimized for every image. The results obtained by the proposed method are compared with the classical AWT method, the context based method (CBD), and the enhanced CBD (ECB) method. It is observed that the proposed injection approach has better performance than the conventional weighted detail injection schemes. © 2016 IEEE.Article Ground-Penetrating Radar Clutter Removal via 1D Fast Subband Decomposition(Defence Scientific Information Documentation Centre, 2019) Kumlu, Deniz; Karasakal, Gokhan; Kaplan, Nur Huseyin; Erer, IsinTarget detection performance in ground-penetrating radar (GPR) deteriorates highly in the presence of clutter. Multi-scale (wavelet transfonn) or the recently proposed multi-scale and multi-directional decomposition based methods can efficiently remove the clutter, however they have high computational complexity. In this paper, we propose a new multi-scale method which requires only 1D fast subband decomposition of the rows of the GPR image. The resulting detail layers directly provide the clutter-free target component of the GPR image. The proposed method is compared to the state-of-art clutter removal methods both visually and quantitatively using a realistic simulated dataset which is constructed by the gprMax simulation software. The results show that the proposed 1D subband decomposition scheme approximates the classical 2D wavelet decomposition successfully and even presents a performance increase as well as a complexity decrease for fast decomposition methods based on lifting wavelet transform and a trous wavelet transform.Article New Color Channel Driven Physical Lighting Model for Low-Light Image Enhancement(Academic Press Inc Elsevier Science, 2025) Kucuk, S.; Severoglu, N.; Demir, Y.; Kaplan, N. H.Outdoor imaging systems, affected by low-light conditions, generally produce low-quality images with poor visibility. Low-quality images can directly influence high-level tasks such as surveillance and autonomous navigation systems. Enhancing the images captured under inadequate lighting conditions aims to generate higher visual quality in these images. However, current low-light enhancement methods may result in color unnaturalness, information loss, and strange artifacts. We propose a new color channel-driven physical lighting model (NCC-PLM) to respond to these issues to improve image quality. More concretely, we first apply a gamma correction to the input image according to its darkness degree, which is determined by its average intensity value. Then, we introduce a new color channel prior to estimate the environmental light (EL) and light scattering attenuation rate (LSAR). Finally, the enhanced image is obtained through the estimations and physical lighting model. Experimental results on various datasets demonstrate the proposed method's effectiveness and superiority over the compared methods both visually and qualitatively. Specifically, we enhance the visual quality of low- light images by revealing intricate details and maintaining color consistency, leading to a natural appearance.Article Evaluation of Trends and Dominant Modes in Maximum Flows in Turkey Using Discrete and Additive Wavelet Transforms(ASCE-Amer Soc Civil Engineers, 2020) Yilmaz, Muhammet; Tosunoglu, Fatih; Kaplan, Nur HuseyinThis paper aims to define trends and dominant modes in annual instantaneous maximum flows (AIMF) covering the period 1961-2014 from 10 gauge stations located in different river basins in Turkey. To achieve this aim, discrete wavelet transform (DWT) and additive wavelet transform (AWT) in conjunction with the Mann-Kendall (MK) test are used and compared for the first time. Moreover, global wavelet spectrum (GWS) is employed to test the significance of the most effective periodic components. The sequential MK test is also used to determine the start or change points of trend in AIMF series. From the MK trend results, five stations showed a statistically significant (at a 5% level) negative trend for AIMF series and short-term periodic components (2 and 4 years) were found to be the most effective components, which are responsible for producing a real trend founded on the data series. The GWS analysis indicated that the most dominant components identified are significant. In addition, the MK-z values of the most effective periods derived from AWT generally showed a better agreement with MK-z value of original time series with higher correlation coefficient compared to those of DWT. The sequential MK graphs of the AWT-based time series also produced a better harmony with the sequential MK of the original data. Finally, the results showed AWT coupled with the MK provided a very efficient and accurate analysis for defining the most effective modes in the AIMF series and can be successfully used in any hydrological time series. (C) 2020 American Society of Civil Engineers.Article Real-World Image Dehazing with Improved Joint Enhancement and Exposure Fusion(Academic Press Inc Elsevier Science, 2023) Kaplan, Nur HuseyinIn this work, a single image dehazing method that improves the haze removal capacity of the Joint Contrast Enhancement and Exposure Fusion (CEEF) method with Smoothing-Sharpening Image Filter (SSIF) is presented. In this method, the hazy image is first sharpened with SSIF to obtain a sharper image. In this way, the difference between haze and objects is amplified. Then, the AHE procedure in CEEF is replaced by CLAHE to obtain an enhanced CEEF. The enhanced CEEF is applied to the filtering result to obtain the final dehazed image. Observations demonstrate that the proposed method obtains enhanced results while reducing the amount of haze. The visual and quantitative comparisons between the proposed method and state-of-the-art dehazing methods show that the proposed method has better dehazing performance and has a 50% improvement in terms of the FADE metric compared to the closest result.Article Single Image Dehazing Based on Multiscale Product Prior and Application to Vision Control(Springer London Ltd, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.In this paper, a novel dehazing algorithm based on multiscale product (MSP) prior is presented. First, the observed hazy image is decomposed into its approximation and detail subbands by undecimated Laplacian decomposition. Then the MSPs of the approximation subbands for each band of the image are calculated to obtain the MSP prior. This prior keeps the significant information of the image, whereas it is capable of detecting the haze in the image. By the use of this prior and the hazy image model, a fast and robust dehazing algorithm is obtained. The proposed method is compared with commonly used methods. The results demonstrate that the proposed algorithm is better than the former methods. Being a fast and robust algorithm, the proposed method has also been applied to a real-time robotic vision control system.Article Remote Sensing Image Enhancement Using Hazy Image Model(Elsevier GmbH, 2018) Kaplan, N. H.In this paper, an effective and simple enhancement method for remotely sensed images is proposed to improve the visual quality of the image. Proposed method uses the hazy image model for image enhancement. Hazy image model consist of two unknown parameters, the global airlight and the transmission map. The proposed method determines the global airlight and the transmission map, by using simple statistical values (the standard deviation and the mean value) of the original image. The proposed method enhances the images better than the former methods, as well as keeps the original reflectance values of the input image better compared to the traditional remote sensing enhancement methods. (C) 2017 Elsevier GmbH. All rights reserved.Conference Object Fast And Effective Clutter Reduction İn Ground Penetrating Radar Using Lifting Wavelet Transform(Institute of Electrical and Electronics Engineers Inc., 2016) Karasakal, G.; Erer, I.; Kaplan, N.H.Clutter, which is occurred by noise which has more intensive reflection than the buried objects such as noise of the transmitter and receiver antennas and the ground surface reflection interference, makes the identification of targets in Ground Penetrating Radar (GPR). Aim of this paper is to achieve a novel, more effective and faster clutter reduction method by using Lifting Wavelet Transform (LWT). LWT is applied on various scenarios and compared with Stationary Wavelet Transform (SWT) which is used very often in clutter and noise removal. In conclusion, LWT provides better and faster results. © 2016 IEEE.Conference Object Remote Sensing Image Enhancement Via Robust Guided Filtering(Institute of Electrical and Electronics Engineers Inc., 2019) Kaplan, N.H.; Erer, I.Remote sensing image enhancement methods have to preserve the original reflectance values as possible as they can, whereas emphasising the edges and increasing the contrast. In this study, a remote sensing image enhancement method based on robust guided filtering is proposed. We propose a multiscale decomposition with the robust guided filtering to obtain the approximation and detail layers of the image. Then the extracted details are amplified and added to the approximation layer to obtain the enhanced image. Both visual and quantitative comparisons show that the proposed method has a better preservation capability than the former methods, as well as a better contrast improvement along-with edge enhancement. © 2019 IEEE.
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