Multi Frame Video Super Resolution Using Convolutional Neural Networks
Video super resolution remains a challenging problem despite being a very active area of research. Even with huge strides made with single image super resolution, multiframe techniques, which utilize multiple frames in improving the quality of a given frame, have yet to fully take advantage of the power of deep learning. We propose a HR for multi frame super resolution that outputs a higher resolution version of a given frame using pixel information from adjacent frames in the video. Unlike traditional multiframe techniques, our proposed method has minimal data pre processing and computation cost. Using both still image and video training data we explore different network architectures and hyperparameter combinations for both the single and multi frame model that give the best results. Testing our solution and comparing to recently developed techniques, we observe promising results.