School of Engineering Department of Computer Science and Engineering 106 Deep Video Super-resolution Supervisor: CHEN Qifeng / CSE Student: HU Wenbin / COMP Course: UROP1100, Summer Autonomous driving (AD) can significantly change people’s life. It will emancipate the labor force and save many resources by reducing the number of vehicles and travel time because cars can be scheduled by computers. Due to the promising future of autonomous driving, I have a keen interest in AD works, so I surveyed some works about AD in this summer UROP project and will keep focusing on AD in the future. AD’s work pipeline encompasses several parts: Maps and Sensors, Perception, Prediction, Planning, and Control, as shown in figure 1. In this UROP project, I mainly focused on motion forecasting for self-driving vehicles. Figure 1. Autonomous driving working pipeline. Deep Video Super-resolution Supervisor: CHEN Qifeng / CSE Student: LIN Siyan / SENG WANG Zixuan / SENG ZHANG Liyu / SENG Course: UROP1100, Summer UROP1100, Summer UROP1100, Summer Image denoising has been a heated topic in image restoration for a long time, and the convolutional neural network (CNN) has attracted considerable attention due to its favorable denoising performance. In this report, we trained and evaluated three different CNN based models on their denoising performance, namely the ResNet, the UNet, and the Noise2Void. We used the same dataset for evaluation and compared their statistical results for denoising. Our results showed that ResNet performed best especially when the noise level is low, while UNet and Noise2Void are more stable. From this perspective, we reflected on the issues we encountered and proposed possible directions for further improvement.
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