Foundations Of Image Science

By M. Grunwald. 2016

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Deep Learning Techniques for Inverse Problems in Imaging (2020, methodology): “This general model is used throughout computational imaging [1], from basic restoration tasks like deblurring, super-resolution, and image inpainting [2], to a wide variety of tomographic imaging applications, including magnetic resonance imaging [3], X-ray computed tomography [4], and radar imaging”
Prior Image-Constrained Reconstruction using Style-Based Generative Models (2021, unknown): “Several digital imaging systems can be approximately modeled by a linear imaging model, described as (Barrett & Myers, 2013) where f ∈ n is a vector that approximates the object to-be-imaged, g ∈ m corresponds to the imaging measurements, and n ∈ m represents the measurement noise.”