Browsing by Author "Kaplan, N. H."

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Acgc: Adaptive Chrominance Gamma Correction for Low-Light Image Enhancement
(Academic Press Inc. Elsevier Science, 2025) Severoglu, N.; Demir, Y.; Kaplan, N. H.; Kucuk, S.
Capturing high-quality images becomes challenging in low-light conditions, often resulting in underexposed and blurry images. Only a few works can address these problems simultaneously. This paper presents a low- light image enhancement scheme based on the Y-I-Q transform and bilateral filter in least squares, named ACGC. The method involves applying a pre-correction to the input image, followed by the Y-I-Q transform. The obtained Y component is separated into its low and high-frequency layers. Local gamma correction is applied to the low-frequency layers, followed by contrast limited adaptive histogram equalization (CLAHE), and these layers are added up to produce an enhanced Y component. The remaining I and Q components are also enhanced with local gamma correction to provide images with amore natural color. Finally, the inverse Y-I-Q transform is employed to create the enhanced image. The experimental results demonstrate that the proposed approach yields superior visual quality and more natural colors compared to the state-of-the-art methods.
Fusion of Multifocus Images by Lattice Structures
(Academic Press Inc Elsevier Science, 2016) Kaplan, N. H.; Erer, I.; Ersoy, O.
Image fusion methods based on multiscale transform (MST) suffer from high computational load due to the use of fast Fourier transforms (ffts) in the lowpass and highpass filtering steps. Lifting wavelet scheme which is based on second generation wavelets has been proposed as a solution to this issue. Lifting Wavelet Transform (LWT) is composed of split, prediction and update operations all implemented in the spatial domain using multiplications and additions, thus computation time is highly reduced. Since image fusion performance benefits from undecimated transform, it has later been extended to Stationary Lifting Wavelet Transform (SLWT). In this paper, we propose to use the lattice filter for the MST analysis step. Lattice filter is composed of analysis and synthesis parts where simultaneous lowpass and highpass operations are performed in spatial domain with the help of additions/multiplications and delay operations, in a recursive structure which increases robustness to noise. Since the original filter is designed for the undecimated case, we have developed undecimated lattice structures, and applied them to the fusion of multifocus images. Fusion results and evaluation metrics show that the proposed method has better performance especially with noisy images while having similar computational load with LSWT based fusion method. (C) 2016 Elsevier Inc. All rights reserved.
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.
Pixel-Wise Low-Light Image Enhancement Based on Metropolis Theorem
(Academic Press Inc Elsevier Science, 2024) Demir, Y.; Kaplan, N. H.; Kucuk, S.; Severoglu, N.
Images taken in low-light conditions frequently encounter visibility problems, such as severe noise, reduced brightness, and low contrast. This paper introduces an approach to enhance low-light images using the Metropolis Theorem (MT). The method begins by applying a global gamma correction to the input image, followed by transforming the globally corrected image into the HSV (Hue, Saturation, Value - V) domain. To achieve multi-scale decomposition, an application of the MT is proposed, resulting in approximation and detail sub-images of the V component. Subsequently, local gamma correction is employed on both the final approximation and detail images to enhance local contrast. The refined approximation and detail images are then combined to reconstruct the refined V component. The reconstructed image is obtained by weighting each band of the image with the refined V component. Experimental results demonstrate that the proposed method outperforms state-of-the-art methods, providing improved visual quality and more natural colors.
Real-Time Dehazing Via Multiscale Products for Vision Control
(IEEE, 2017) Kaplan, N. H.; Ayten, K. K.; Dumlu, A.
In this study, a dehazing algorithm based on multiscale product (MSP) prior is presented. In this method, 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 is 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.
Remote Sensing Image Enhancement by Rolling Guidance and Hazy Image Model
(IEEE, 2021) Kaplan, N. H.; Erer, I
An efficient image enhancement method should improve the contrast in the image while keeping the edge and color information. Since existing approaches seem not to fulfill all these demands, a hybrid approach which will combine advantages of individual approaches is proposed in this work. The multiscale bilateral filter is replaced by an iterative joint version where the output is used as guidance image for the next iterations. Then a multiscale structure is designed by the appropriate modifications of the spatial and range kernels as in the multiscale bilateral filter. A final refining is performed by the local use of the Hazy Image Model based method (HIM) on the resulting image. Visual and quantitative comparisons with conventional Discrete Wavelet Transform and Singular Value Decomposition based method (DWT-SVD), Regularized Histogram Equalization with Discrete Cosine Transform method (RHE-DCT), Bilateral Filtering based method (BF), and HIM method demonstrate the superiority of the proposed method and the resulting hybrid method for remote sensing image enhancement.
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.
Scale Aware Remote Sensing Image Enhancement Using Rolling Guidance
(Academic Press Inc Elsevier Science, 2021) Kaplan, N. H.; Erer, I
Enhancement of remotely sensed images is a challenging problem, since the enhanced image has to have an improved contrast and edge information while preserving the original radiance values as much as possible. In this paper, a scale aware enhancement method based on rolling guidance is proposed for remotely sensed images. For each scale, a guidance image is defined and the approximation image is provided by an iterative joint filtering of the approximation and guidance images. Then the extracted details are amplified through an adaptive scheme and added to the final level approximation layer to provide the resulting enhanced image. A comparative study between the proposed methods with classical edge preserving filters and traditional methods have been carried out by using several criteria. The proposed methods have an average of 12% improvement for contrast gain (CG) metric and 81% improvement for enhancement measurement (EME) metric compared to the closest comparison method.
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.