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Image Quality Assessment (IQA) remains a complex and challenging problem that has garnered great interest by the research community as well as industry. A majority of no-reference IQA metrics leverage handcrafted features which discriminates distortions from true image structures. Recently, deep neural networks (DNN) has been successfully applied on local image patches to estimate image quality, However,...
Magnetic resonance (MR) images of higher quality is demanded for helping with more accurate and earlier diagnosis of different diseases. The overall quality of MR images is limited due to the existence of different degradation factors such as (1) MR aberrations due to intrinsic properties of the MR scanner, (2) magnetic field inhomogeneity, and (3) inherent MRI noise. Correcting each MRI degradation...
A novel generalized sampling-based probabilistic scale space theory is proposed for image restoration. We explore extending the definition of scale space to better account for both noise and observation models, which is important for producing accurately restored images. A new class of scale-space realizations based on sampling and probability theory is introduced to realize this extended definition...
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