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|Title:||Deep image restoration and enhancement||Authors:||Fu, Zixuan||Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2022||Publisher:||Nanyang Technological University||Source:||Fu, Z. (2022). Deep image restoration and enhancement. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/158695||Project:||D-258-21221-03521||Abstract:||This thesis mainly focuses on image denoising, an important part of image restoration and enhancement which attempts to recover a noise-free image from a noisy version. Recently, deep learning denoising methods have outperformed many traditional model-based denoising methods. These methods handle the denoising problem by training a deep convolutional neural network in a supervised-learning manner, given a large dataset consisting of paired noisy and clean images. However, this scheme fails in some circumstances since well-aligned noisy and clean images sometime are hard to obtain. To solve this problem, this thesis considers a more general and practical unsupervised-learning setting for image denoising, which is achieving image denoising by utilizing unpaired noisy and clean images. However, training a denoising network with unpaired images directly is almost impossible. Thus, we separate the unsupervised denoising problem into an unsupervised noise generation problem and a supervised denoising problem. To be more specific, a generative model is first applied to learn the noise distribution from the noisy images, and synthesizes paired clean and noisy images. This stage is called the noise generation stage. Then the unpaired denoising problem degrades to a paired denoising problem, and a denoising network can be easily trained in a supervised-learning manner, called the denoising stage. Several experiments on synthetic noise dataset show our proposed method is promising to solve this unpaired denoising problem.||URI:||https://hdl.handle.net/10356/158695||Fulltext Permission:||restricted||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Theses|
Updated on Dec 1, 2022
Updated on Dec 1, 2022
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