Vision-based driving assistance systems has made a paradigm shift in the automotive industry, especially in auto electronics, towards vehicular technology in smart cities. This technology concentrates on safeguarding the members inside and outside the car, such as pedestrians or other vehicles on the road. This paper proposes a methodology that employs a single image/frame-based rain filter for video processing. In this paper, an innovative method of frame filtering is statistically modelled to provide a clear vision on the road during rainy weather. The frame filtering displays an improvement by considering a reference frame as the input frame and it is further exploited to confiscate the contextual and atmospheric particles using the method of frame difference. Moreover, this article suggests a hybrid algorithm that accomplishes filtering utilizing L0-gradient image smoothing and weights least square smoothing along with adaptive histogram equalization to preserve and enhance the image under intense rainy conditions. With a single frame-based filtering methodology, the proposed algorithm performs well with rainy images but loses information during video processing. The proposed method displays an improvement by 36.29 % and 3.48 % in Structural Similarity Index (SSIM) and 57.15 % and 14.47 % in Gradient Magnitude Similarity Deviation (GMSD) as compared to guided filter and bilateral filter methods.

Enhancing visual clarity in rainy conditions based on single-frame filtering algorithm

Pau, Giovanni;
2024-01-01

Abstract

Vision-based driving assistance systems has made a paradigm shift in the automotive industry, especially in auto electronics, towards vehicular technology in smart cities. This technology concentrates on safeguarding the members inside and outside the car, such as pedestrians or other vehicles on the road. This paper proposes a methodology that employs a single image/frame-based rain filter for video processing. In this paper, an innovative method of frame filtering is statistically modelled to provide a clear vision on the road during rainy weather. The frame filtering displays an improvement by considering a reference frame as the input frame and it is further exploited to confiscate the contextual and atmospheric particles using the method of frame difference. Moreover, this article suggests a hybrid algorithm that accomplishes filtering utilizing L0-gradient image smoothing and weights least square smoothing along with adaptive histogram equalization to preserve and enhance the image under intense rainy conditions. With a single frame-based filtering methodology, the proposed algorithm performs well with rainy images but loses information during video processing. The proposed method displays an improvement by 36.29 % and 3.48 % in Structural Similarity Index (SSIM) and 57.15 % and 14.47 % in Gradient Magnitude Similarity Deviation (GMSD) as compared to guided filter and bilateral filter methods.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/166588
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