Low Illumination Color Image Enhancement Based on Improved Retinex Theory

Wang, Ping; Wang, Zhiwen; Lv, Dong; Zhang, Canlong; Wang, Yuhang

doi:10.1145/3443467.3443777

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Therefore, in order to extract citrus surface defects accurately, it is necessary to correct the surface brightness. Currently, commonly used brightness correction methods include Retinex-based algorithm [7] , histogram equalization (HE) [8] , brightness correction based on illuminationreflectance model [3] and other methods. The goal of these methods is to obtain images with appropriate contrast and details.…”

Section: Image Brightness Correctionmentioning

confidence: 99%

Segmentation method of citrus surface defect based on brightness correction

Nie

2023

International Conference on Optics and Machine Vision (ICOMV 2023)

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The uneven distribution of light brightness on the surface of citrus spheres will make it difficult to directly partition the defect area of citrus surface. In view of the above shortcomings, a segmentation method of citrus surface defect based on brightness correction was proposed. In this method, Otsu algorithm was used to segment the background in HSI color space, and the mask template was obtained after the holes were filled. The mask template and I component were obtained by dot multiplication to obtain the I component image with the background removed. The I component image of the removed background was convolved with the established multi-scale Gaussian function to obtain the incident component image, and then the I component image after correction was obtained by point division with the incident component image. Finally, the single threshold method was used to extract citrus surface defects. The overall accuracy of this method is 94.7%, the recognition rate of normal fruit is 95%, and the recognition rate of defective fruit is 96.5%. It was found that the main reasons for misjudgment were that the color of the defect was similar to the normal fruit, the defect area was eliminated during denoising due to too small, and the convex or fold on the surface of the citrus was judged as the defect. This paper proposes an image brightness correction algorithm based on multi-scale Gaussian function, which can effectively correct citrus surface brightness and provide technical support for segmentation defects.

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Section: Image Brightness Correctionmentioning

confidence: 99%

Segmentation method of citrus surface defect based on brightness correction

Nie

2023

International Conference on Optics and Machine Vision (ICOMV 2023)

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“…However, the traditional Retinex algorithm is prone to lose edge detail information and produce false halos, so scholars have proposed a variety of methods to effectively improve the traditional Retinex algorithm. Liu et al proposed a fast algorithm based on Retinex, which can enhance lowilluminance images and restore information lost by low-illumination [5]; Wang et al proposed a low-illuminance color image enhancement algorithm based on Gabor filter and Retinex theory, the restored color information in the processed image is closer to the original image [6].…”

Section: Introductionmentioning

confidence: 99%

Character Recognition of Low Illumination Product Marking Based on Vision

Shen,

Chen,

Fan

et al. 2024

Advances in Transdisciplinary Engineering

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This paper is mainly aimed at recognition of the horizontal indeterminate-length marking characters of products under low illumination conditions. We first obtain images to be processed from local galleries or the camera. Then, we apply multi-scale Retinex algorithm in HSV space to improve the brightness of images and denoising technology to improve the quality of images. Finally, we realize text detection through CTPN network model and character recognition through CRNN network model. End-to-end character recognition is achieved. The experimental results show that the accuracy of the CTPN network model is 80.91%, and the accuracy of the CRNN network model is 61.79% respectively.

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Low Illumination Color Image Enhancement Based on Improved Retinex Theory

Cited by 2 publications

References 6 publications

Segmentation method of citrus surface defect based on brightness correction

Segmentation method of citrus surface defect based on brightness correction

Character Recognition of Low Illumination Product Marking Based on Vision

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