Low-light Image Enhancement Based on Directional Total Variation Retinex

Zhang, Jie; Zhou, Pucheng; Mei, Xue

doi:10.3724/sp.j.1089.2018.16965

Cited by 6 publications

(4 citation statements)

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“…Retinex is a common method of image enhancement based on scientific experiments and scientific analysis, which is proposed by Edwin.H.Land in 1963 ( Land and McCann, 1971 ). In this theory, two factors determine the color of an object being observed, as shown in Figure 1 , namely the reflective properties of the object and the intensity of the light around the them, but according to the theory of color constancy, it is known that the inherent properties of the object are not affected by light, and the ability of the object to reflect different light waves determines the color of the object to a large extent ( Zhang et al, 2018 ).…”

Section: Basic Theorymentioning

confidence: 99%

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Sun

Zhao

et al. 2022

Front. Bioeng. Biotechnol.

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In order to solve the problems of poor image quality, loss of detail information and excessive brightness enhancement during image enhancement in low light environment, we propose a low-light image enhancement algorithm based on improved multi-scale Retinex and Artificial Bee Colony (ABC) algorithm optimization in this paper. First of all, the algorithm makes two copies of the original image, afterwards, the irradiation component of the original image is obtained by used the structure extraction from texture via relative total variation for the first image, and combines it with the multi-scale Retinex algorithm to obtain the reflection component of the original image, which are simultaneously enhanced using histogram equalization, bilateral gamma function correction and bilateral filtering. In the next part, the second image is enhanced by histogram equalization and edge-preserving with Weighted Guided Image Filtering (WGIF). Finally, the weight-optimized image fusion is performed by ABC algorithm. The mean values of Information Entropy (IE), Average Gradient (AG) and Standard Deviation (SD) of the enhanced images are respectively 7.7878, 7.5560 and 67.0154, and the improvement compared to original image is respectively 2.4916, 5.8599 and 52.7553. The results of experiment show that the algorithm proposed in this paper improves the light loss problem in the image enhancement process, enhances the image sharpness, highlights the image details, restores the color of the image, and also reduces image noise with good edge preservation which enables a better visual perception of the image.

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Section: Basic Theorymentioning

confidence: 99%

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Sun

Zhao

et al. 2022

Front. Bioeng. Biotechnol.

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“…With the development of variational technology, image enhancement method based on Retinex theory has been widely concerned by researchers [7]. Retinex method has the characteristics of enhancing local contrast, compressing dynamic range, and maintaining image color constancy [8].…”

Section: Related Research On Technology Of Low Light Image Preprocessingmentioning

confidence: 99%

An Image Preprocessing Model of Coal and Gangue in High Dust and Low Light Conditions Based on the Joint Enhancement Algorithm

Gong

2021

Computational Intelligence and Neuroscience

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The lighting facilities are affected due to conditions of coal mine in high dust pollution, which bring problems of dim, shadow, or reflection to coal and gangue images, and make it difficult to identify coal and gangue from background. To solve these problems, a preprocessing model for low-quality images of coal and gangue is proposed based on a joint enhancement algorithm in this paper. Firstly, the characteristics of coal and gangue images are analyzed in detail, and the improvement ways are put forward. Secondly, the image preprocessing flow of coal and gangue is established based on local features. Finally, a joint image enhancement algorithm is proposed based on bilateral filtering. In experimental, K-means clustering segmentation is used to compare the segmentation results of different preprocessing methods with information entropy and structural similarity. Through the simulation experiments for six scenes, the results show that the proposed preprocessing model can effectively reduce noise, improve overall brightness and contrast, and enhance image details. At the same time, it has a better segmentation effect. All of these can provide a better basis for target recognition.

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“…Image enhancement methods leveraging Retinex decompose the original image into its illumination and reflection components, aiding in the preservation of detailed information to a certain extent. 3 Subsequently, researchers and experts consistently advocated sophisticated approaches, such as single/multi-scale Retinex 4,5 (SSR/MSR) and multi-scale Retinex with color restoration (MSRCR). 6 Wei et al 7 introduced RetinexNet, a method grounded in Retinex principles that attained accelerated image processing speeds.…”

Section: Introductionmentioning

confidence: 99%

“…However, the introduction of noise often compromises intricate features and fails to meet the exacting standards for detail enhancement in this study. Image enhancement methods leveraging Retinex decompose the original image into its illumination and reflection components, aiding in the preservation of detailed information to a certain extent 3 . Subsequently, researchers and experts consistently advocated sophisticated approaches, such as single/multi-scale Retinex 4 , 5 (SSR/MSR) and multi-scale Retinex with color restoration (MSRCR) 6 .…”

Section: Introductionmentioning

confidence: 99%

Enhancement of dark areas on the surface of scrap metals based on RGB-NIR image fusion

Ma,

Ye,

et al. 2024

J. Electron. Imag.

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The application of machine vision in object identification and classification has significantly enhanced recognition efficiency. Nevertheless, for non-ferrous scrap metals with poor surface smoothness, the unevenness of reflected light results in the generation of dark regions in the images, obscuring a considerable amount of detailed information and reducing the recognition accuracy. Addressing these challenges, we propose a method for enhancing the details of dark regions based on the RGB-NIR image fusion theory, integrating detailed information from NIR images into RGB images. First, a robust deep residual denoising network is constructed to estimate and remove noise in images. Subsequently, to address the difficulty of extracting structural features in dark regions, a multi-scale spatial deep structure feature extraction module based on channel attention blocks is developed. This module effectively extracts the structural features of RGB and NIR image pairs, with the target image serving as the supervisory signal. Finally, guided by the theory of structural inconsistency, multi-scale feature maps are fused. The image fusion network adopts an encoder-decoder architecture embedded with residual channel attention blocks. The experimental results indicate that the approach proposed in this study demonstrates notable efficacy in image denoising and detail enhancement.

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Low-light Image Enhancement Based on Directional Total Variation Retinex

Cited by 6 publications

References 0 publications

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

Low-Illumination Image Enhancement Algorithm Based on Improved Multi-Scale Retinex and ABC Algorithm Optimization

An Image Preprocessing Model of Coal and Gangue in High Dust and Low Light Conditions Based on the Joint Enhancement Algorithm

Enhancement of dark areas on the surface of scrap metals based on RGB-NIR image fusion

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