MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement

Zhang, Xin; Wang, Xia

doi:10.1109/access.2021.3068534

Cited by 22 publications

(7 citation statements)

References 43 publications

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“…It can also process the color information and gray information in the image separately. In the application of image enhancement, it can achieve good color retention and enhancement performance at the same time [21,22]. HSI color model is completely different from the above two color models based on physics or process, which is -a perception based color model [23].…”

Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

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In order to solve the problems of low brightness contrast of a color image, hiding a large amount of detail information, and deviation of color information in the process of image acquisition, an optimization method of plane image color enhancement processing based on computer vision virtual reality is proposed. In this method, the input RGB image is converted into the image represented by the HSI color model, and its adaptive brightness is adjusted to improve the overall brightness of the image. For the local detail enhancement of the color image, the three-dimensional Gaussian model perceived by retinal neurons is introduced into the illuminance image estimation of the MSR algorithm to enhance the image color. The results are as follows: from the perspective of objective parameter evaluation, the mean, standard deviation, information entropy, and average gradient of example images 1 and 2 are improved by about 70%; this algorithm not only enhances the brightness and contrast of the image but also maintains the detailed edge information of the image and the color characteristics of the object itself. The average enhancement rate is the highest among various algorithms, up to 95%. The algorithm proposed in this paper maintains the edge detail information of the image, optimizes the defects of the combination of traditional bilateral filtering and Retinex algorithm, and the color is also well restored, which makes the monitoring image easier to identify, more conducive to criminal investigation and solving cases, and lays a foundation for subsequent image processing.

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Section: Color Modelmentioning

confidence: 99%

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Xue

Ming

Lian

2022

Mathematical Problems in Engineering

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“…However, their performance is often unsatisfactory, and Lighten-Net [31] unnaturally brightens the center of the image, which varies from the ground truth. Furthermore, the definition of the ground-truth illumination and reflectance elements is not clear, which makes it difficult to guide the training process [33]. Recently, Zero-DCE [9] proposed incisive and simple nonlinear curve mapping with the help of non-reference loss functions; however, as the method only depends on the non-reference loss function, oversaturation of the colors can often be observed in the enhanced results.…”

Section: B Deep Learning-basedmentioning

confidence: 99%

“…x O y and σ y L are the variance-and covariance-predicted and ground-truth images, respectively. Constants c 1 and c 2 are used to prevent the denominator from being zero (c 1 = 0.0001, c 2 = 0.0009 is the same as that used in [33]). The SSIM loss function requires grayscale images.…”

Section: Loss Functionmentioning

confidence: 99%

LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

2022

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Images captured in low-brightness environments often lead to poor visibility and exhibit artifacts such as low brightness, low contrast, and color distortion. These artifacts not only affect the visual perception of the human eye but also decrease the performance of computer vision algorithms. Existing deep learning-based image enhancements studies are quite slow and usually require extensive hardware specifications. Conversely, lightweight enhancement approaches do not provide satisfactory performance as compared to state-of-the-art methods. Therefore, we proposed a fast and lightweight deep learning-based algorithm for performing low-light image enhancement using the light channel of Hue Saturation Lightness (HSL). LiCENt stands for Light Channel Enhancement Network that uses a combination of an autoencoder and convolutional neural network (CNN) to train a low-light enhancer to first improve the illumination and later improve the details of the low-light image in a unified framework. This method used a single channel lightness 'L' of HSL color space instead of traditional RGB color channels which helps in reducing the number of learnable parameters by a factor of 8.92, at the most. LiCENt also has significant advantages for the Brilliance Perception Adjustment, which enables the model to avoid issues including over-enhancement and color distortion. The experimental results demonstrate that our approach generalizes well in synthetic and natural low-light images and outperforms other methods in terms of qualitative and quantitative metrics.

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“…In recent years, with the rapid development of machine learning, an increasing number of researchers have applied it to video image processing [ 11 , 12 , 13 , 14 , 15 ]. Based on the theory of traditional algorithms, some learning models have been created.…”

Section: Related Workmentioning

confidence: 99%

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

Guo

Jia

Yang

et al. 2021

Sensors

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In low illumination situations, insufficient light in the monitoring device results in poor visibility of effective information, which cannot meet practical applications. To overcome the above problems, a detail preserving low illumination video image enhancement algorithm based on dark channel prior is proposed in this paper. First, a dark channel refinement method is proposed, which is defined by imposing a structure prior to the initial dark channel to improve the image brightness. Second, an anisotropic guided filter (AnisGF) is used to refine the transmission, which preserves the edges of the image. Finally, a detail enhancement algorithm is proposed to avoid the problem of insufficient detail in the initial enhancement image. To avoid video flicker, the next video frames are enhanced based on the brightness of the first enhanced frame. Qualitative and quantitative analysis shows that the proposed algorithm is superior to the contrast algorithm, in which the proposed algorithm ranks first in average gradient, edge intensity, contrast, and patch-based contrast quality index. It can be effectively applied to the enhancement of surveillance video images and for wider computer vision applications.

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MARN: Multi-Scale Attention Retinex Network for Low-Light Image Enhancement

Cited by 22 publications

References 43 publications

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

Optimization of Plane Image Color Enhancement Processing Based on Computer Vision Virtual Reality

LiCENt: Low-Light Image Enhancement Using the Light Channel of HSL

Detail Preserving Low Illumination Image and Video Enhancement Algorithm Based on Dark Channel Prior

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