Gaussian Pyramid Transform Retinex Image Enhancement Algorithm Based on Bilateral Filtering

Chang, 林昌 Lin; Haifeng, 周海峰 Zhou; Wu, 陈武 Chen

doi:10.3788/lop57.161019

Cited by 2 publications

(6 citation statements)

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“…However, it failed to achieve the purpose of enlarging the gray difference between the background and foreground. It can be seen from Figure 6(c) that the de-noising performance of the algorithm in [14] is relatively improved. However, the highfrequency noise processing is not perfect, and the images have different degrees of uneven brightness.…”

Section: Results and Analysismentioning

confidence: 95%

“…The larger the value, the smaller the image distortion. It can be seen from Table 1 that the anti noise performance of reference [14], reference [15] and the algorithm in this paper has been improved compared with the single scale Retinex algorithm on the whole. The algorithm in this paper has the most significant improvement, with an increase of 24.23%, and an average increase of 15.43% compared with reference [14], and an average increase of 12.65% compared with reference [15].…”

Section: Objective Evaluationmentioning

confidence: 87%

“…After the sigmoid functs A pixels, the enhancement effect of Retinex algorithm can be retained. To verify the effectiveness and robustness of the algorithm in this paper, five lawn weed images in different growth states as shown in Figure 6 are selected and compared with the single scale Retinex enhancement fused with bilateral filtering and Gaussian pyramid 14 , multiscale Retinex algorithm for dynamic gray stretching respectively 15 . The results are shown in Figure 6.…”

Section: S(x)=mentioning

confidence: 99%

“…When the number of pixels to be subdivided is small, the operation time of algorithm in this paper is only second to Retinex enhancement algorithm. When the number of pixels to be subdivided is large, running time of the algorithm in this paper is equivalent to that in reference [14], but it still has advantages in peak signal to noise ratio. Therefore, the algorithm in this paper can meet the real-time requirements of engineering practice.…”

Section: Objective Evaluationmentioning

confidence: 99%

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Recognition of lawn weeds based on retinex algorithm with local density fusion

化,

李,

张

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

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In order to solve the problem of difficulty in segmenting the foreground of lawn weed images due to the similarity between the foreground and background grayscale, this paper proposes a Retinex enhancement algorithm based on local density fusion. Firstly, image preprocessing is completed based on local variance to realize prominent image foreground and smooth cluttered background; Secondly, using multi threshold segmentation and operational differentiation, pixels are divided into three categories: foreground, background, and pixels to be subdivided. Local density is used to extract spatial information of pixels to be subdivided; Finally, the sigmoid function is used to optimize the reflection component grayscale transformation coefficient, achieving the fusion of local density information and obtaining the final image. The results show that the algorithm proposed in this paper expands the gray difference between weeds and lawn grass while suppressing background noise; Compared with other enhancement algorithms, the peak signal-to-noise ratio, average running time, and segmentation accuracy have all been improved greatly. At the same time, through image analysis, it has been found that the algorithm results in occasional problems of uneven brightness and over segmentation of fine leaf weeds in the image. In response to these two problems, this article finally proposes two corresponding optimization measures, namely global brightness adjustment based on a new adaptive gamma function and Retinex reflection component saturation adjustment based on AHE.

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Section: Results and Analysismentioning

confidence: 95%

Section: Objective Evaluationmentioning

confidence: 87%

Section: S(x)=mentioning

confidence: 99%

Section: Objective Evaluationmentioning

confidence: 99%

See 2 more Smart Citations

Recognition of lawn weeds based on retinex algorithm with local density fusion

化,

李,

张

2024

Fifteenth International Conference on Signal Processing Systems (ICSPS 2023)

View full text Add to dashboard Cite

show abstract

“…The frequency domain enhancement methods mainly include Fourier transform, wavelet transform, Kalman filtering and image pyramid, etc ( Li et al, 2019c ; Li et al, 2019d ; Huang et al, 2019 ; Chang et al, 2020 ; Tian et al, 2020 ; Liu et al, 2021c ). This kind of algorithm can effectively enhance the structural features of the image, but the target details of the image which are enhanced by these methods are still blurred.…”

Section: Related Workmentioning

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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Gaussian Pyramid Transform Retinex Image Enhancement Algorithm Based on Bilateral Filtering

Cited by 2 publications

References 0 publications

Recognition of lawn weeds based on retinex algorithm with local density fusion

Recognition of lawn weeds based on retinex algorithm with local density fusion

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

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