Abstract
Visual degradation of captured images caused by rainy streaks under rainy weather can adversely affect the performance of many open-air vision systems. Hence, it is necessary to address the problem of eliminating rain streaks from the individual rainy image. In this work, a deep convolution neural network (CNN) based method is introduced, called Rain-Removal Net (R2N), to solve the single image de-raining issue. Firstly, we decomposed the rainy image into its high-frequency detail layer and low-frequency base layer. Then, we used the high-frequency detail layer to input the carefully designed CNN architecture to learn the mapping between it and its corresponding de-rained high-frequency detail layer. The CNN architecture consists of four convolution layers and four deconvolution layers, as well as three skip connections. The experiments on synthetic and real-world rainy images show that the performance of our architecture outperforms the compared state-of-the-art de-raining models with respects to the quality of de-rained images and computing efficiency.
Highlights
IntroductionWith the development of computer, network communication, image processing electronic and other related fields, the intelligent video surveillance system which based on video and image processing has achieved many promising progresses
In recent years, with the development of computer, network communication, image processing electronic and other related fields, the intelligent video surveillance system which based on video and image processing has achieved many promising progresses
From Tab. 2, we can see that the proposed architecture achieves the highest structural similarity index (SSIM) values on both four synthesized rainy images, which verifies the effectiveness of the proposed architecture
Summary
With the development of computer, network communication, image processing electronic and other related fields, the intelligent video surveillance system which based on video and image processing has achieved many promising progresses. The system plays an extremely role in maintaining public security In such condition, some computer vision tasks that related to the intelligent surveillance system have become hot research topics, such as target tracking, track identity, and object detection [Garg and Nayar (2004)] etc. This condition may occur if some safe accidents happened in public areas are recorded by surveillance cameras or mobile phones in rainy days. In this circumstance, the captured video or image data contains a large number of rain streaks, which leads to the refractions and reflections of important contents and distorts the image signal as well as reduces the image quality. In order to maintain the performances of the security-related outdoor monitoring systems and to enhance the visual quality of the degraded images, it is essential to remove rain streaks automatically from the single images captured under a rainy condition
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