Author: Li, Zhecheng
Title: Research on deep neural networks on image motion deblurring
Advisors: Li, Ping (COMP)
Degree: M.Sc.
Year: 2021
Subject: Image processing -- Digital techniques
Neural networks (Computer science)
Hong Kong Polytechnic University -- Dissertations
Department: Department of Computing
Pages: v, 50 pages : color illustrations
Language: English
Abstract: With the widespread availability of all kinds of cameras and smartphones today, taking pictures has become one of the most common activities in people's daily lives. However, due to object movement, hand shake, etc., the pictures are often blurred. Therefore, an effective dynamic scene deblurring algorithm is of great research value. Dynamic scene deblurring has drawn growing attention in recent years. Traditional methods often observe various statistical patterns in the image to derive much a priori knowledge to build a mathematical model to construct an optimization problem, but the results are usually modest and time-consuming. The widespread use of convolutional neural networks in computer vision has made deep learning models the dominant solution in recent years. Although recent deep learning-based methods have made significant progress, the models also have a lot of room for improvement. Through our research, we found that recent deep learning models are usually focus on spatial features, and thus, lack the capability to deal with blurry images in various scenarios. To tackle this problem, We investigate the effect of receptive field and nonlinear mapping ability on network performance separately. For receptive field, we introduce ResInception module to achieve different scales of receptive field by processing images with different size of convolutional layers simultaneously. We also introduce TV(Total Variation For nonlinear mapping ability, we introduce the feature attention module to mine the channel features of the image, and use the feature transformation module with nested skip connections to enhance the nonlinear capability of the network. Through experimentation, our network has had comparable performance to state-of-the-art algorithms, not only qualitatively, but also quantitatively.
Rights: All rights reserved
Access: restricted access

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