Examination Committee

Prof Wa Hung LEUNG, CHEM/HKUST (Chairperson)
Prof Matthew MCKAY, ECE/HKUST (Thesis Supervisor)
Prof Yebin LIU, Department of Automation, Tsinghua University (External Examiner)
Prof Roger CHENG, ECE/HKUST
Prof Weichuan YU, ECE/HKUST
Prof Andrew HORNER, CSE/HKUST

Abstract

The Fourier transform in image processing is an important tool that allows observation of an image in the frequency-domain and due to which several difficult problems become very simple to analyze. The transform is used in a wide range of applications, such as image analysis, image filtering, image compression, etc. On the other hand, with the recent trend for data-driven approaches, deep learning has arisen as a promising learning method that has had success in several computer vision areas, such as image classification, object detection, pedestrian detection, etc. Deep learning algorithms train a deep neural network on a large set of images to learn the parameters instead of using hand-tuned filters.
 
In this work, we propose to design a framework, inspired by both the Fourier transform and deep-learning methodology, to address two image reconstruction problems: multispectral demosaicking and low-dose computed tomography (LDCT) artifact reduction. In multispectral demosaicking, we need to reconstruct the original image from an overly downsampled image, whereas in LDCT artifact reduction, we need to reconstruct an artifact-free image from an artifact-induced image. Basically, in both these problems, we must restore the original component from a given noisy observation and thus require a proper investigation to understand the fundamental aspects of both problems. In view of this observation, in this work, we first analyze the both problems in the frequency-domain and then propose a solution that addresses both problems. Specifically, our proposed framework for each problem can be divided into two phases:

1.    Frequency-domain analysis phase: With the help of frequency-domain analysis, we are able to identify the potential issues in the assumptions of each problem and then give a systematic analysis of why existing methods cannot produce a result with a sufficient level of quality.
2.    Reconstruction phase: Based on the frequency-domain analysis, we first propose an algorithm based on an image-driven approach, which extracts information from the target image itself to address the problems. Later, a data-driven approach is incorporated into the proposed algorithm to further enhance its performance. In the data-driven approach, we first train a deep neural network on a large set of images and then address the problems with the learned parameters.