Image rain removal aims to separate the background image from the rainy image. During the past three years, the image rain removal with deep convolutional neural networks has achieved impressive performance. However, how to reach tradeoff between high de-raining performance and low model parameters is still a challenge. To address the issue, the paper is devoted to exploring a novel method based on wavelet deep recursive pyramid convolution residual network (WDRPRN), in which discrete wavelet transform is embedded to decompose the rainy image in different frequency domains, and the deep recursive pyramid convolution residual network (DRPRN) can well predict the residual coefficients between rainy image and clean image. In addition, compared with other neural networks, the DRPRN adopts recursive model that can cost fewer parameters. Plentiful of experiments on synthetic and real-world datasets show that the proposed method is significantly superior to the recent state-of-the-art algorithms. INDEX TERMS Image rain removal, wavelet transform, residual coefficients, low model parameters.
Nowadays almost all the park toll management system in use is based on charging by operators, up and down the barrier gate manually or the motorman get through barrier gates by swiping card. This paper try to solve the problem by designed a intelligent park system based on image processing. The system capture video frames through camera, recognizing the vehicle license by pattern recognition, sending command throng CAN bus to control the barrier gate, what is more, the states of control system is shown on LCM.The system has realized automatic control of barrier gates without parking, which, of course, slows down the vehicle wear and tear, greatly improves the efficiency, reaching the goal of green, energy saving and environmental protecting.
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