Authors:
Hiroki Inoue
1
;
Keisuke Doman
1
;
Jun Adachi
2
and
Yoshito Mekada
1
Affiliations:
1
Graduate School of Engineering, Chukyo University, Toyota, Aichi, Japan
;
2
Aisin Seiki Co., Ltd., Kariya, Aichi, Japan
Keyword(s):
Raindrop Removal, Vehicle Camera Video, Deep Learning, Optical Flow.
Abstract:
This paper proposes a recursive framework for raindrop removal in a vehicle camera video considering the temporal consistency. Raindrops attached to a vehicle camera lens may prevent a driver or a camera-based system from recognizing the traffic environment. This research aims to develop a framework for raindrop detection and removal in order to deal with such a situation. The proposed method sequentially and recursively restores a video containing no raindrops from original one that may contain raindrops. The proposed method uses an output (restored) image as one of the input frames for the next image restoration process in order to improve the restoration quality, which is the key concept of the proposed framework. In each restoration process, the proposed method first detects raindrops in each input video frame, and then restores the raindrop regions based on the optical flow. The optical flow can be calculated in the outer part of the raindrop region more accurately than the inne
r part due to the difficulty of finding a corresponding pixel, which is the assumption for designing the proposed method. We confirmed that the proposed framework has the potential for improving the restoration accuracy through several preliminary experiments and evaluation experiments.
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