Compressed Sensing and Imaging Processing—2nd Edition

Dear Colleagues,

COMPRESSED SENSING (CS) is an emerging theory which ensures that a sparse signal can be reconstructed from very few incoherent measurements. It is applied in plenty of frontier fields, such as Internet of Things, wireless sensor networks, biomedical applications etc. Compressed sensing techniques allow to significantly reduce the amount of data to be acquired and thereby accelerates data acquisition, reduces motion artefacts, and lowers radiation exposure. In compressed sensing, iterative algorithms based on prior information have been applied for image reconstruction. In this Special Issue, original papers are invited in the area of Compressive Sensing Applications to Biomedical Images and Signals. Biomedical instruments and systems could benefit tremendously from compressive sensing in many areas, such as efficient data acquisition, low-power sensing, solving inverse problems, sparse coding, machine learning, and distributed network sensing applications such as Internet of Things.

Prof. Dr. Lixiang Li
Guest Editor

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• Compressed Sensing and Imaging Processing in Sensors (11 articles)