Compressed Sensing in Biomedical Signal and Image Analysis

Dear Colleagues,

Sampling of analog signals using the classical Shannon–Nyquist theorem has created and enhanced the digital world we all currently live in. Around 2004, researchers in the field of information theory published a series of seminal papers in which they demonstrated that provided that signals/images exhibit some sort of sparsity, they could be reconstructed using far fewer samples than the number typically needed with the classical sampling techniques. This led to the new paradigm of Compressive Sensing and a great deal of applications in all domains of the digital world. 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. Sri Krishnan
Guest Editor

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