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• W3210719995 abstract "In a low-illumination environment, the contrast between lane lines and the ground is relatively low. Traditional image enhancement algorithms, such as gamma correction, Histogram Equalization, and multiple-scale Retinex, may result in over enhancement and detail loss, which decreases the detection accuracy of driver assistance systems. In this work, we introduce a low-illumination image enhancement algorithm based on improved Retinex theory and apply it to lane-line detection. A luminance channel optimization method based on a bimodal energy function is adopted to select the weight of the linear combination. A guided filter with edge-preservation is applied to obtain the illumination component of the scene. Furthermore, the reflection component is estimated in a hyperbolic tangent space, and the luminance and contrast are adaptively adjusted to obtain the enhanced image. Experimental results show that proposed method can effectively extract the lane-line edges and suppress the noise in the dark area with low lane-line illumination. Moreover, it can improve the detection success rate of an assisted driving system." @default.
• W3210719995 created "2021-11-08" @default.
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• W3210719995 date "2021-10-27" @default.
• W3210719995 modified "2023-09-27" @default.
• W3210719995 title "Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter" @default.
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• W3210719995 doi "https://doi.org/10.1080/08839514.2021.1997212" @default.
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