Abstract
Rapid developments in urbanization and smart city environments have accelerated the need to deliver safe, sustainable, and effective resource utilization and service provision and have thereby enhanced the need for intelligent, real-time video surveillance. Recent advances in machine learning and deep learning have the capability to detect and localize salient objects in surveillance video streams; however, several practical issues remain unaddressed, such as diverse weather conditions, recording conditions, and motion blur. In this context, image de-raining is an important issue that has been investigated extensively in recent years to provide accurate and quality surveillance in the smart city domain. Existing deep convolutional neural networks have obtained great success in image translation and other computer vision tasks; however, image de-raining is ill posed and has not been addressed in real-time, intelligent video surveillance systems. In this work, we propose to utilize the generative capabilities of recently introduced conditional generative adversarial networks (cGANs) as an image de-raining approach. We utilize the adversarial loss in GANs that provides an additional component to the loss function, which in turn regulates the final output and helps to yield better results. Experiments on both real and synthetic data show that the proposed method outperforms most of the existing state-of-the-art models in terms of quantitative evaluations and visual appearance.
Highlights
Rain is a common weather condition that negatively impacts computer vision systems.Raindrops appear as bright streaks in images due to their high velocity and light scattering.Since image recognition and detection algorithms are designed for clean inputs, it is essential to develop an effective mechanism for rain streak removal.A number of research efforts have been reported in the literature focusing on restoring rain images, and different approaches have been taken
We propose a conditional generative adversarial network-based framework for rain streak removal
The paper is organized as follows: In Section 2, we provide an overview of related methods for image de-raining and the basic concepts behind conditional generative adversarial networks (cGANs)
Summary
Rain is a common weather condition that negatively impacts computer vision systems. Raindrops appear as bright streaks in images due to their high velocity and light scattering. When defining the objective function, it should consider the fact that the performance of vision algorithms, such as classification/detection, should not be affected by the presence of rain streaks The addition of this discriminative information ensures that the output is indistinguishable from its original counterpart. We propose a conditional generative adversarial network-based framework for rain streak removal. Traditional GANs tend to make output images more artificial and visually displeasing To mitigate this issue, we have introduced a conditional CNN with skip connections for the generator. The paper is organized as follows: In Section 2, we provide an overview of related methods for image de-raining and the basic concepts behind cGANs. Section 3 describes the proposed model (CGANet—Conditional Generative Adversarial Network model) in detail with its architecture.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
