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About

Joonki Paik is a leading figure in digital image processing and computer vision, whose work bridges fundamental theory and practical imaging systems. His research primarily focuses on image restoration, computational photography, and robust video analysis. Paik's major contributions include pioneering real-time object tracking with optical flow-based active feature models, which has garnered 88 citations, and developing innovative depth estimation techniques using a multiple color-filter aperture camera—a novel approach that enables multifocusing from a single capture. His work on fast image restoration for spatially varying defocus blur has been instrumental in enhancing imaging sensor performance, while his robust video stabilization methods, employing particle keypoint updates and l1-optimized camera paths, have advanced consumer and professional videography. Paik's impact is reflected in his highly cited papers, including his widely used textbook "Fundamentals of Digital Image Processing" (2017). He has also contributed comprehensive surveys on feature detectors and descriptors, guiding researchers in the field. Beyond these achievements, Paik has explored stereo vision for autonomous mobile robots and illuminant-invariant matching, demonstrating the breadth of his expertise. His research continues to shape how digital cameras capture, stabilize, and interpret visual information, making him a key resource for students and engineers in imaging science.