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Indigo Deep Learning For Image Reconstruction | Erin Mills Town Centre
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Many problems in science, engineering and medicine follow an inverse approach to problem by observations the output data to calculate or predict the inputs should be to generated the responses: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. Recent advance in deep learning-based algorithms has emerged as a novel paradigm for image processing.This book compiles the state-of-the-art approaches for solving inverse problems by deep learning; from basic concepts to deep learning and algorithms in image processing. It serves as an introduction to researchers working in image processing, and pattern recognition as well as students undertaking research in signal processing and AI.The book will include the following:
Many problems in science, engineering and medicine follow an inverse approach to problem by observations the output data to calculate or predict the inputs should be to generated the responses: for example, calculating an image in X-ray computed tomography, source reconstruction in acoustics, or calculating the density of the Earth from measurements of its gravity field. Recent advance in deep learning-based algorithms has emerged as a novel paradigm for image processing.This book compiles the state-of-the-art approaches for solving inverse problems by deep learning; from basic concepts to deep learning and algorithms in image processing. It serves as an introduction to researchers working in image processing, and pattern recognition as well as students undertaking research in signal processing and AI.The book will include the following:
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