Low-light image restoration using bright channel prior-based variational Retinex model

Park, Seonhee; Moon, Byeongho; Ko, Seungyong; Yu, Shuhui; Paik, Joonki

doi:10.1186/s13640-017-0192-3

Cited by 25 publications

(11 citation statements)

References 24 publications

(52 reference statements)

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“…Experimental results of [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]46,47] using an image sensor detected intrusion objects in a general environment with normal illumination. Although many object detection systems adopt a low-light enhancement algorithm as preprocessing [2,3], it still cannot properly work if there are is no illumination at all. Even if there is a very small amount of illumination, a general low-light enhancement algorithm usually amplifies noise as well as image intensity.…”

Section: Resultsmentioning

confidence: 99%

“…Performance of a general vision-based surveillance system is limited under a low-illumination condition. Although many low-light image enhancement methods were recently proposed [2,3], the surveillance function does not work if there is no light. To illuminate an empty indoor space to acquire high-quality images, high power consumption is unavoidable.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

Park

Kim

et al. 2018

Symmetry

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This paper presents a novel hybrid sensor-based intrusion detection system for low-power surveillance in an empty, sealed indoor space with or without illumination. The proposed system includes three functional steps: (i) initial detection of an intrusion event using a sound field sensor; (ii) automatic lighting control based on the detected event, and (iii) detection and tracking the intruder using an image sensor. The proposed hybrid sensor-based surveillance system uses a sound field sensor to detect an abnormal event in a very low-light or completely dark environment for 24 h a day to reduce the power consumption. After detecting the intrusion by the sound sensor, a collaborative image sensor takes over an accurate detection and tracking tasks. The proposed hybrid system can be applied to various surveillance environments such as an office room after work, empty automobile, safety room in a bank, and armory room. This paper deals with fusion of computer-aided pattern recognition and physics-based sound field analysis that reflects the symmetric aspect of computer vision and physical analysis

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

Park

Kim

et al. 2018

Symmetry

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“…Tao et al combined a bright channel prior with a convolutional neural network (CNN) [240], and Park et al combined a bright channel prior [241] with a Retinex enhancement algorithm. Both achieved improved results [242]. A fast enhancement algorithm for low-light video has been proposed by combining Retinex theory with dark channel prior theory [243], and this algorithm can be further combined with scene detection, edge compensation and interframe compensation techniques for video enhancement.…”

Section: F Methods Based On Defogging Modelsmentioning

confidence: 99%

An Experiment-Based Review of Low-Light Image Enhancement Methods

et al. 2020

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Images captured under poor illumination conditions often exhibit characteristics such as low brightness, low contrast, a narrow gray range, and color distortion, as well as considerable noise, which seriously affect the subjective visual effect on human eyes and greatly limit the performance of various machine vision systems. The role of low-light image enhancement is to improve the visual effect of such images for the benefit of subsequent processing. This paper reviews the main techniques of low-light image enhancement developed over the past decades. First, we present a new classification of these algorithms, dividing them into seven categories: gray transformation methods, histogram equalization methods, Retinex methods, frequency-domain methods, image fusion methods, defogging model methods and machine learning methods. Then, all the categories of methods, including subcategories, are introduced in accordance with their principles and characteristics. In addition, various quality evaluation methods for enhanced images are detailed, and comparisons of different algorithms are discussed. Finally, the current research progress is summarized, and future research directions are suggested. INDEX TERMS Review, survey, low-light image enhancement, Retinex method, image enhancement, quality evaluation.

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“…Histogram equalization is an adaptive enhancement for difference contrast image by adjusting or stretching grey levels into a uniformly distributed histogram. The histogram matching is redistributed the histogram of a specified image according to a given other image [24,25,26,27].Although global approach is suitable for adjusting overall contrast, brightness and distribution of gray scale. However, there are differenet local areas which need to speical enhancing process.…”

Section: Intensity Image Enhancementmentioning

confidence: 99%

A Comprehensive and Practical Image Enhancement Method

2019

KSII TIIS

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Image enhancement is a challenging problem in the field of image processing, especially low-light color images enhancement. This paper proposed a robust and comprehensive enhancement method based several points. First, the idea of bright channel is introduced to estimate the illumination map which is used to attain the enhancing result with Retinex model, and the color constancy is keep as well. Second, in order eliminate the illumination offsets wrongly estimated, morphological closing operation is used to modify the initial estimating illumination. Furthermore, in order to avoid fabricating edges, enlarged noises and over-smoothed visual features appearing in enhancing result, a multi-scale closing operation is used. At last, in order to avoiding the haloes and artifacts presented in enhancing result caused by gradient information lost in previous step, guided filtering is introduced to deal with previous result with guided image is initial bright channel. The proposed method can get good illumination map, and attain very effective enhancing results, including dark area is enhanced with more visual features, color natural and constancy, avoiding artifacts and over-enhanced, and eliminating Incorrect light offsets.

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Low-light image restoration using bright channel prior-based variational Retinex model

Cited by 25 publications

References 24 publications

Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

Hybrid Sensor Network-Based Indoor Surveillance System for Intrusion Detection

An Experiment-Based Review of Low-Light Image Enhancement Methods

A Comprehensive and Practical Image Enhancement Method

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