An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Mahmood, Awais; Khan, Sajid Ali; Hussain, Shariq; Almaghayreh, Eslam Mohammad

doi:10.1109/access.2019.2951468

Cited by 44 publications

(16 citation statements)

References 46 publications

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“…The HE is a simple yet efficient image contrast enhancement technique, however, output images are often over enhanced. Mahmood et al solves the problem of the loss of details and unnatural look to the target image in HE-based techniques [40]. The results processed by Retinex-based methods have halo artifact sometimes.…”

Section: Related Workmentioning

confidence: 99%

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Zhu

Zhang

et al. 2020

Multimed Tools Appl

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Motivation: Medical image enhancement is a crucial part to improve the quality of the images. The excellent visual effects and image quality can help doctors make quick diagnoses. Among medical images, Magnetic Resonance Imaging (MRI) images play a vital role in clinical diagnosis. Its imaging principle highlights the human tissue part ignoring the boundary information sometimes. Moreover, some imaging results lose details in visual due to the low contrast and the quality of the images. To overcome these limitations, we propose an MRI enhancement method based on visual-attention by means of contrast adjustment and illumination component preservation. Description: The proposed framework includes image generation and image fusion to tackle the limitation of a single image. First, we assume an MRI image composed of tissues and details. We design an adaptive attenuation weight matrix based on the input MRI image according to a new definition of pixel energy. Then, an illumination-preserving image is introduced into the model for the attenuated image as compensation. Finally, an effective image fusion decision map calculation method is devised to create an enhanced MRI image with higher contrast and better perceptual quality. Results and conclusion: The experimental results show that it is a more effective enhancement method which has better performance on most of the objective evaluation metrics and stability than other 14 methods as well as maintains the balance between contrast and illumination of enhanced MRI images.

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Section: Related Workmentioning

confidence: 99%

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Zhu

Zhang

et al. 2020

Multimed Tools Appl

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“…Many methods have been proposed for image contrast enhancement [9]. A recent example is the adaptive contrast enhancement method that preserves the image brightness, proposed by Khan et al [26]. By transforming image to the wavelet domain they leverage the multiresolution analysis.…”

Section: Constrast Enhancementmentioning

confidence: 99%

Fast eyes detection in thermal images

Knapik

Cyganek

2020

Multimed Tools Appl

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In recent years many methods have been proposed for eye detection. In some cases however, such as driver drowsiness detection, lighting conditions are so challenging that only the thermal imaging is a robust alternative to the visible light sensors. However, thermal images suffer from poor contrast and high noise, which arise due to the physical properties of the long waves processing. In this paper we propose an efficient method for eyes detection based on thermal image processing which can be successfully used in challenging environments. Image pre-processing with novel virtual high dynamic range procedure is proposed, which greatly enhances thermal image contrast and allows for more reliable computation of sparse image descriptors. The bag-of-visual-words approach with clustering was selected for final detections. We compare our method with the YOLOv3 deep learning model. Our method attains high accuracy and fast response in real conditions without computational complexity and requirement of a big dataset associated with the deep neural networks. For quantitative analysis a series of thermal video sequences were recorded in which eye locations were manually annotated. Created dataset was made publicly available on our website.

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“…One of the most common degradations that affect digital images is the low-contrast effect [5], which can occur due to lack of user experience, faulty camera settings, hardware or software limitations, nature of the environment, and deficient lighting conditions [6]. The contrast is usually described as the variance between the darkest and the brightest values of the image [7]. It is considered an essential feature that describes the lucidity of details in an image [8].…”

Section: Introductionmentioning

confidence: 99%

Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

Al-Ameen¹

2021

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Contrast is a distinctive image feature that tells if it has adequate visual quality or not. On many occasions, images are captured with low-contrast due to inevitable obstacles. Therefore, an improved type-II fuzzy set-based algorithm is developed to enhance the contrast of various color and grayscale images properly while preserving the brightness and providing natural colors. The proposed algorithm utilizes new upper and lower ranges, amended Hamacher t-conorm, and a transform-based gamma correction method to provide the enhanced images. The proposed algorithm is assessed with artificial and real contrast distorted images, compared with twelve specialized methods, and the outcomes are evaluated using four advanced metrics. From the obtained results of experiments and comparisons, the developed algorithm demonstrated the ability to process various color and grayscale images, performed the best among the comparative methods, and scored the best in all four quality evaluation metrics. The findings of this study are significant because the proposed algorithm has low-complexity and can adjust the contrast of different images expeditiously, which enables it to be used with different imaging modalities especially those with limited hardware resources or produce high-resolution images.

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An Adaptive Image Contrast Enhancement Technique for Low-Contrast Images

Cited by 44 publications

References 46 publications

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

MRI enhancement based on visual-attention by adaptive contrast adjustment and image fusion

Fast eyes detection in thermal images

Contrast Enhancement of Digital Images Using an Improved Type-II Fuzzy Set-Based Algorithm

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