Browsing by Author "Demir, Y."

Now showing 1 - 3 of 3

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.
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.