Image Adaptive Contrast Enhancement for Low-illumination Lane Lines Based on Improved Retinex and Guided Filter
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 a...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Taylor & Francis Group
2021-12-01
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| Series: | Applied Artificial Intelligence |
| Online Access: | http://dx.doi.org/10.1080/08839514.2021.1997212 |
| Summary: | 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. |
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| ISSN: | 0883-9514 1087-6545 |