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UID:5087-1627912800-1627916400@ece.northeastern.edu
SUMMARY:ECE PhD Dissertation Defense: Yulun Zhang
DESCRIPTION:PhD Dissertation Defense: Deep Convolutional Neural Network for Image Restoration and Synthesis \nYulun Zhang \nLocation: Zoom Link \nAbstract: Image restoration and synthesis with deep learning play a fundamental role in the computer vision community. They are widely used on mobile devices (e.g.\, smartphones) or lead to billion-dollar startups. However\, how to design efficient deep convolutional neural networks (CNNs) to extract higher-quality deep CNN features for better image restoration and synthesis is still challenging. In this dissertation talk\, I will describe my recent works to enhance CNN features in the channel dimension or/and the spatial dimensions. First\, for image restoration\, I will briefly introduce our proposed residual dense network. Then\, I will introduce the residual in residual (RIR) structure to train very deep super-resolution networks. Such an RIR structure could also make the network learn more high-frequency information\, being critical for high-resolution output. Attention mechanism (e.g.\, channel attention and spatial attention) is further explored to highlight the features. Second\, for image synthesis\, I will introduce multimodal style transfer via graph cuts. I visualize the deep features and find the multimodal style representation. I then formulate the style matching problem as an energy minimization one\, which could be solved via graph cuts. As a result\, the transferred features contain spatially semantic information\, providing more visually pleasing stylized results. Besides\, we investigate image synthesis about texture hallucination with large scaling factors. We propose an efficient high-resolution hallucination network for very large scaling factors.
URL:https://ece.northeastern.edu/event/ece-phd-dissertation-defense-yulun-zhang/
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