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A Survey On Astronomical Image Deconvolution
PhD Qualifying Examination Title: "A Survey On Astronomical Image Deconvolution" by Miss Yixin LIU Abstract: Image deconvolution is a technique to remove noise and sharpen the contrast in a “dirty” image, and has been applied on widefield as well as microscopic images to improve image quality. This survey focuses on radio astronomical images, which are generated from radio telescopes. Representative deconvolution algorithms on these images are either iterative or statistical. Iterative methods select a subset of pixels to clean in each iteration whereas statistical algorithms build a mathematical model to fit the entire image. As a result, iterative methods work well on images of point sources and statistical algorithms excel on those of extended objects. However, statistical algorithms are computationally more expensive and consume more memory than iterative methods. As both point sources and extended objects are studied in radio astronomical images, we discuss possible improvements on both kinds of deconvolution methods, for example, adding new features in iterative algorithms to enhance quality, and parallelizing statistical methods to improve time efficiency. Date: Monday, 13 June 2022 Time: 2:00pm - 4:00pm Zoom Meeting: https://hkust.zoom.us/j/98126430868?pwd=UitzbDI1akc5eEN6Y3BNNE5CVTJIdz09 Committee Members: Prof. Qiong Luo (Supervisor) Prof. Pedro Sander (Chairperson) Dr. Qifeng Chen Dr. Dan Xu **** ALL are Welcome ****