High-Dimensional and Low-Quality Visual Information Processing

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The thesis delves into intelligent sensing and the interpretation of high-dimensional, low-quality visual information. It investigates the intrinsic structures of visual data and introduces various computational models that encompass diverse mathematical fields such as compressive sensing and graph theory. These models are utilized to tackle real-world challenges, including biometric recognition, stereo signal reconstruction, natural scene parsing, and synthetic aperture radar (SAR) image processing, showcasing their practical applications and effectiveness.