Comprehensive Review of Video Enhancement Algorithms for Low Lighting Conditions

Vishalakshi, G. R.; Gopalakrishna, M. T.; Hanumantharaju, M. C.

doi:10.1007/978-81-322-2757-1_47

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…The author ZM did part experiments of the paper, ZM, GZ and CZ revised the paper in different version of the paper, respectively. The contributions of the proposed work are mainly in two aspects: (1) We propose an accuracy transmission estimation method based on bright channel prior and dark channel prior. We firstly utilize the bright channel prior to get an initial transmission estimation and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior.…”

Section: Availability Of Data and Materialsmentioning

confidence: 99%

“…Images captured in nighttime low illumination usually suffer from a poor visibility (as shown in Fig. 1), and has negatively impacts on subsequent processing for outdoor computer vision applications [1]. Thereby, to ensure the reliable of outdoor vision systems under nighttime, it is highly desirable to enhance the visibility of low illumination images.…”

Section: Introductionmentioning

confidence: 99%

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Nighttime low illumination image enhancement with single image using bright/dark channel prior

Shi

Zhu

Guo

et al. 2018

J Image Video Proc.

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Nighttime low illumination image enhancement is highly desired for outdoor computer vision applications. However, few works have been studied towards this goal. In addition, the low illumination enhancement problem becomes very challenging when the depth information of a low illumination image is unknown. To address this problem, in this paper, we propose a dual channel prior-based method for nighttime low illumination image enhancement with a single image, which builds upon two existing image priors: dark channel prior and bright channel prior. We utilize the bright channel prior to get an initial transmission estimate and then use the dark channel as a complementary channel to correct potentially erroneous transmission estimates attained from the bright channel prior. Experimental results show significant credibility of the approach both visually and by quantitative comparison with existing methods.

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Section: Availability Of Data and Materialsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Nighttime low illumination image enhancement with single image using bright/dark channel prior

Shi

Zhu

Guo

et al. 2018

J Image Video Proc.

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show abstract

“…Due to the influence of insufficient light such as night, cloudy weather, rain and fog, and backlight shooting, the collected images are prone to problems such as low dynamic range, loss of detail, and serious noise, which cause the degradation of image quality and limit the performance of computer vision system [1–3]. In order to be able to provide high‐quality images for subsequent image analysis and processing, image enhancement of low‐light images is required.…”

Section: Introductionmentioning

confidence: 99%

Low‐light image enhancement based on exponential Retinex variational model

Chen

Hua

2021

IET Image Processing

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Aiming at the problems of residual noise, low contrast, and limited detail information caused by low-light images, this paper proposes a new Retinex variational model. According to Retinex theory, it is necessary to estimate the illumination and reflectance components decomposed from the original image. In order to better maintain the edge information, texture richness, and prevent artefacts, the exponential forms of local variation deviation and total variation are used as illumination prior and reflectance prior, respectively, and mixed norms are used to constrain them, so as to deal with the illumination information and texture details of the image more effectively, and then use the bright channel prior to improve the colour reproduction sense of the original image, thereby constructing the objective function, and finally using the alternating iterative optimization method to find the optimal solution to the proposed model. Experiments show that compared with other existing image enhancement methods, the method proposed here improves the contrast of the image, overcomes the phenomenon of halo artefacts and colour distortion, is more consistent with human vision, and produces better results in terms of quantitative performance.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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