A low-light image enhancement method for both denoising and contrast enlarging

Li, Lin; Wang, Ronggang; Wang, Wenmin; Gao, Wen

doi:10.1109/icip.2015.7351501

Cited by 172 publications

(91 citation statements)

References 15 publications

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“…3. This idea has been recently explored in several works related to low-light image enhancement [35,4,28,15]. The above result can be regarded as providing a theoretical support to these works.…”

Section: The Duality Between Retinex and Image Dehazingsupporting

confidence: 60%

On the Duality Between Retinex and Image Dehazing

Galdrán

Bria

Alvarez-Gila

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

113

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Image dehazing deals with the removal of undesired loss of visibility in outdoor images due to the presence of fog. Retinex is a color vision model mimicking the ability of the Human Visual System to robustly discount varying illuminations when observing a scene under different spectral lighting conditions. Retinex has been widely explored in the computer vision literature for image enhancement and other related tasks. While these two problems are apparently unrelated, the goal of this work is to show that they can be connected by a simple linear relationship. Specifically, most Retinex-based algorithms have the characteristic feature of always increasing image brightness, which turns them into ideal candidates for effective image dehazing by directly applying Retinex to a hazy image whose intensities have been inverted. In this paper, we give theoretical proof that Retinex on inverted intensities is a solution to the image dehazing problem. Comprehensive qualitative and quantitative results indicate that several classical and modern implementations of Retinex can be transformed into competing image dehazing algorithms performing on pair with more complex fog removal methods, and can overcome some of the main challenges associated with this problem.

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Section: The Duality Between Retinex and Image Dehazingsupporting

confidence: 60%

On the Duality Between Retinex and Image Dehazing

Galdrán

Bria

Alvarez-Gila

et al. 2018

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition

113

View full text Add to dashboard Cite

show abstract

“…Thus, a variety of image enhancement methods have been studied [32,33,34,35,36,37,38,39]. Traditional methods include histogram equalization (HE) and histogram specification (HS) [32].…”

Section: Related Workmentioning

confidence: 99%

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

Kim

Hong

Park

2017

Sensors

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Because intelligent surveillance systems have recently undergone rapid growth, research on accurately detecting humans in videos captured at a long distance is growing in importance. The existing research using visible light cameras has mainly focused on methods of human detection for daytime hours when there is outside light, but human detection during nighttime hours when there is no outside light is difficult. Thus, methods that employ additional near-infrared (NIR) illuminators and NIR cameras or thermal cameras have been used. However, in the case of NIR illuminators, there are limitations in terms of the illumination angle and distance. There are also difficulties because the illuminator power must be adaptively adjusted depending on whether the object is close or far away. In the case of thermal cameras, their cost is still high, which makes it difficult to install and use them in a variety of places. Because of this, research has been conducted on nighttime human detection using visible light cameras, but this has focused on objects at a short distance in an indoor environment or the use of video-based methods to capture multiple images and process them, which causes problems related to the increase in the processing time. To resolve these problems, this paper presents a method that uses a single image captured at night on a visible light camera to detect humans in a variety of environments based on a convolutional neural network. Experimental results using a self-constructed Dongguk night-time human detection database (DNHD-DB1) and two open databases (Korea advanced institute of science and technology (KAIST) and computer vision center (CVC) databases), as well as high-accuracy human detection in a variety of environments, show that the method has excellent performance compared to existing methods.

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“…This unit receives the sampled video frame from the video capture unit. The images from the video capture unit are the RGB image and for the very dim light condition, we perform low-light image enhancement and noise elimination [20]. For improving the accuracy of our system, we eliminate the noise of the image before amplifying it through contrast enhancement.…”

Section: B Face Detection Unit and Features Extractionmentioning

confidence: 99%

“…For this first, we scaled the rectangle boxed in face image to 100*100 pixels and then we use the 80x30 rectangular pixels window for eye and 40x40 rectangular pixels window for the mouth. The x-and ycoordinates of the rectangular window is (10,20) and (30, 60) for the eye and mouth respectively. The above part is then transferred to the fatigue detection unit for further processing.…”

Section: B Face Detection Unit and Features Extractionmentioning

confidence: 99%

An improved fatigue detection system based on behavioral characteristics of driver

Gupta

Aman

Shiva

et al. 2017

2017 2nd IEEE International Conference on Intelligent Transportation Engineering (ICITE)

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In recent years, road accidents have increased significantly. One of the major reasons for these accidents, as reported is driver fatigue. Due to continuous and longtime driving, the driver gets exhausted and drowsy which may lead to an accident. Therefore, there is a need for a system to measure the fatigue level of driver and alert him when he/she feels drowsy to avoid accidents. Thus, we propose a system which comprises of a camera installed on the car dashboard. The camera detect the driver's face and observe the alteration in its facial features and uses these features to observe the fatigue level. Facial features include eyes and mouth. Principle Component Analysis is thus implemented to reduce the features while minimizing the amount of information lost. The parameters thus obtained are processed through Support Vector Classifier for classifying the fatigue level. After that classifier output is sent to the alert unit.

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A low-light image enhancement method for both denoising and contrast enlarging

Cited by 172 publications

References 15 publications

On the Duality Between Retinex and Image Dehazing

On the Duality Between Retinex and Image Dehazing

Convolutional Neural Network-Based Human Detection in Nighttime Images Using Visible Light Camera Sensors

An improved fatigue detection system based on behavioral characteristics of driver

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