The event camera is a novel sensor that records brightness change in the form of asynchronous events with high temporal resolution, and simultaneously outputs intensity images with a lower frame rate. Events recorded by sensors have a lot of noise and the intensity images captured often suffer from motion blur and noise effects. Therefore, to reconstruct high quality images is of great significance for the application of event camera in computer vision. However, the existing reconstruction methods only addressed the motion blur issue without considering the influence of noise. In this paper, we propose a variational model by using spatial smooth constraint regularization to recover clean image frames from blurry and noisy camera images and events at any frame rate. We present experimental results on synthetic dataset as well as real dataset with high speed and high dynamic range to demonstrate that the proposed algorithm is superior to the other reconstruction algorithms.
• Hardware: Processor: i3 ,i5 RAM: 4GB Hard disk: 16 GB • Software: operating System : Windws2000/XP/7/8/10 Anaconda,jupyter,spyder,flask Frontend :-python Backend:- MYSQL
₹10000 (INR)
2020