Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Han, Yi; Chen, Xiangyong; Zhong, Yi; Huang, Yanqing; Li, Zhuo; Han, Ping; Li, Qing; Yuan, Zhenhui

doi:10.3390/electronics12040990

Cited by 6 publications

(9 citation statements)

References 28 publications

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“…Figures 3 and 5 From the results and their histograms, the horizontal axis represents pixel values, while the vertical axis represents the number of pixels. it can be concluded that the frequency of low gray values in the original image is high and the overall image is dark, after enhancement by the method in this paper, the range of gray values is expanded, its contrast is enhanced, and the pixel values of its brighter regions are adjusted while enhancing its overall brightness compared to the method in the literature [4]. As observed from Figure 7, the proposed method enhances the overall brightness, contrast, and sharpness of the image.…”

Section: Bilateral Filteringmentioning

confidence: 88%

“…As observed from Figure 7, the proposed method enhances the overall brightness, contrast, and sharpness of the image. In comparison to the approach of replacing the logarithm function with the sigmoid function and the method described in reference [4], our method shows respective increases in standard deviation by 15% and 4.5%, peak signal-to-noise ratio (PSNR) by 53% and 25%, and image entropy by 4% and 7%. However, due to the adaptive adjustment of pixel values in brighter regions, our method yields a lower mean brightness compared to the previous two methods.…”

Section: Bilateral Filteringmentioning

confidence: 91%

“…However, the high computational complexity of Wavelet Transform leads to poor real-time performance, and its edge effects might cause distortions at the image boundaries. Han et al [4] . used Histogram Equalization and Multiscale Retinex (MSR) to improve overall image brightness while maintaining image details.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Low-light road image enhancement based on optimized MSR

Lan,

Huang,

Huang

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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Utilizing machine vision to acquire road environment information is a crucial factor influencing the performance of advanced driver assistance systems, but under low-light conditions, obtaining high-quality road information directly through vision becomes challenging, necessitating image enhancement. Due to uneven illumination in low-light conditions, existing enhancement methods often lead to issues such as glare and blurred details in bright areas. In this paper, we propose an optimized Multi-Scale Retinex (MSR) image enhancement algorithm.Firstly, RGB images are converted to the YUV format, and the MSR algorithm is applied to enhance the Y channel. The local background brightness is then incorporated into the Just Noticeable Difference (JND) model to establish a non-linear relationship model between background brightness and adjustment factors. This enables adaptive adjustment of image enhancement intensity. Subsequently, a non-linear bilateral filtering function is applied to smooth the adjusted image, followed by Contrast Limited Adaptive Histogram Equalization (CLAHE) to enhance its contrast. Finally, the resulting image is fused with the original image in a 1:1 ratio.Experimental results indicate that, compared to the combination of MSR and histogram equalization, the proposed method achieves a 4.5% increase in standard deviation, a 7% increase in information entropy, and a 25% improvement in peak signal-to-noise ratio.

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Section: Bilateral Filteringmentioning

confidence: 88%

Section: Bilateral Filteringmentioning

confidence: 91%

See 1 more Smart Citation

Low-light road image enhancement based on optimized MSR

Lan,

Huang,

Huang

2024

Third International Conference on Advanced Manufacturing Technology and Electronic Information (AMTEI 2023)

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“…The approaches for enhancing nighttime road scene images include techniques based on histogram equalization 3 , those based on the Retinex theory 4 , strategies based on deep learning 5 and their hybrid methods such as Retinex-based deep unfolding network 6 and histogram equalization multiscale Retinex combination approach 7 . Histogram equalization-based techniques concentrate on reshaping an image’s histogram distribution to achieve a uniform distribution.…”

Section: Related Workmentioning

confidence: 99%

Nighttime road scene image enhancement based on cycle-consistent generative adversarial network

Jia,

Yu,

Chen

et al. 2024

Sci Rep

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During nighttime road scenes, images are often affected by contrast distortion, loss of detailed information, and a significant amount of noise. These factors can negatively impact the accuracy of segmentation and object detection in nighttime road scenes. A cycle-consistent generative adversarial network has been proposed to address this issue to improve the quality of nighttime road scene images. The network includes two generative networks with identical structures and two adversarial networks with identical structures. The generative network comprises an encoder network and a corresponding decoder network. A context feature extraction module is designed as the foundational element of the encoder-decoder network to capture more contextual semantic information with different receptive fields. A receptive field residual module is also designed to increase the receptive field in the encoder network.The illumination attention module is inserted between the encoder and decoder to transfer critical features extracted by the encoder to the decoder. The network also includes a multiscale discriminative network to discriminate better whether the image is a real high-quality or generated image. Additionally, an improved loss function is proposed to enhance the efficacy of image enhancement. Compared to state-of-the-art methods, the proposed approach achieves the highest performance in enhancing nighttime images, making them clearer and more natural.

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“…Histogram equalization is used to change the grayscale value of the image to a form that is evenly distributed throughout the grayscale range. This enhances the contrast of the image by adjusting the dynamic range of the grey values [20]. Figure 4 shows the results of the image processing.…”

Section: Histogram Equalizationmentioning

confidence: 99%

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

Li,

Yin,

Zhang

2023

Sensors

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Crack disease is one of the most serious and common diseases in road detection. Traditional manual methods for measuring crack detection can no longer meet the needs of road crack detection. In previous work, the authors proposed a crack detection method for asphalt pavements based on an improved YOLOv5s model, which is a better model for detecting various types of cracks in asphalt pavements. However, most of the current research on automatic pavement crack detection is still focused on crack identification and location stages, which contributes little to practical engineering applications. Based on the shortcomings of the above work, and in order to improve its contribution to practical engineering applications, this paper proposes a method for segmenting and analyzing asphalt pavement cracks and identifying parameters based on image processing. The first step is to extract the crack profile through image grayscale, histogram equalization, segmented linear transformation, median filtering, Sauvola binarization, and the connected domain threshold method. Then, the magnification between the pixel area and the actual area of the calibration object is calculated. The second step is to extract the skeleton from the crack profile images of asphalt pavement using the Zhang–Suen thinning algorithm, followed by removing the burrs of the crack skeleton image using the connected domain threshold method. The final step is to calculate physical parameters, such as the actual area, width, segments, and length of the crack with images obtained from the crack profile and skeleton. The results show that (1) the method of local thresholding and connected domain thresholding can completely filter noise regions under the premise of retaining detailed crack region information. (2) The Zhang–Suen iterative refinement algorithm is faster in extracting the crack skeleton of asphalt pavement, retaining the foreground features of the image better, while the connected-domain thresholding method is able to eliminate the missed isolated noise. (3) In comparison to the manual calibration method, the crack parameter calculation method proposed in this paper can better complete the calculation of crack length, width, and area within an allowable margin of error. On the basis of this research, a windowing system for asphalt pavement crack detection, WSPCD1.0, was developed. It integrates the research results from this paper, facilitating automated detection and parameter output for asphalt pavement cracks.

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Low-Illumination Road Image Enhancement by Fusing Retinex Theory and Histogram Equalization

Cited by 6 publications

References 28 publications

Low-light road image enhancement based on optimized MSR

Low-light road image enhancement based on optimized MSR

Nighttime road scene image enhancement based on cycle-consistent generative adversarial network

Crack Segmentation Extraction and Parameter Calculation of Asphalt Pavement Based on Image Processing

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