Review of Algorithms for Compressive Sensing of Images
This is an incremental review aimed at beginners in compressive sensing, particularly for LiDAR applications.
The paper provides a comprehensive review of classical algorithms for compressive sensing of images, focusing on Total Variation methods and their application in LiDAR systems, including simulations of real noise and standardized comparisons to guide algorithm selection.
We provide a comprehensive review of classical algorithms for compressive sensing of images, focused on Total variation methods, with a view to application in LiDAR systems. Our primary focus is providing a full review for beginners in the field, as well as simulating the kind of noise found in real LiDAR systems. To this end, we provide an overview of the theoretical background, a brief discussion of various considerations that come in to play in compressive sensing, and a standardized comparison of off-the-shelf methods, intended as a quick-start guide to choosing algorithms for compressive sensing applications.