אירועים והרצאות בפקולטה למדעי המחשב ע"ש הנרי ומרילין טאוב

Compressive sensing is a relatively new theory that is based on the fact that many natural signals (or images) can be sparsely represented in some basis. The theory of compressive imaging is applied to directly capture a compressed version of an object's image. Here, we give an overview of different CI techniques, and focus on two that were developed in our group. While implementing conventional CS for imaging we have encountered some practical implementation issues, which were caused due to the high dimensionality of the images. These issues were addressed by developing a novel two dimensional (2-D) separable sensor. The solution enables the sensing and reconstruction of images having a megapixel size, in contrast to kilopixel size images, which are the current standard in compressive imaging. A reduction of the complexity by 10^6 for megapixel size images is demonstrated both theoretically and empirically.

* A Ph.D. student under the supervision of Dr. Adrian Stern and Prof. Joseph Rosen (BGU).