School of Engineering Department of Computer Science and Engineering 107 Deep Video Super-resolution Supervisor: CHEN Qifeng / CSE Student: VUONG Tung Duong / DSCT Course: UROP1100, Spring The Super-Resolution task in video using deep networks is the main topic of my UROP project. This term, we focus on videos of human faces, which have various application. Using low resolution video, we hope to improve its quality efficiency by a number of idea. The first approach is to use NeRF to learn the 3D presentation of the object and increase the resolution by align the textures from some highresolution images. The second approach is to use C2-Matching, a method that try to match the correspondences between lower images and high resolution reference. Both approach can be applied together for a better result. Moreover, there is room for improvement in both approach. Deep Video Super-resolution Supervisor: CHEN Qifeng / CSE Student: ZHANG Hanning / COSC Course: UROP1100, Summer I participated in a project called Stereo Audio Localization in July and August, during which I collaborated with my schoolmate Huihao, supervised by professor and graduate students. The project’s goal is predicting the location on a reference image based on Stereo footstep. Our mission is to expand the dataset, making it more diverse, and try to improve the machine learning if possible. At the same time, I also spend much time learning how to read through and run a project code of the paper, how to modify the project to meet different requirements. Until now, we have collected totally 37 single person video and 21 double-person video, with on campus and off campus environments. And we have tried to train part of our samples.