What is the reconstruction range for compressive fresnel holography? Conference Paper uri icon


  • Compressive sensing (CS) has attracted much interest since its emergence 6 years ago [1-3]. The theory gave birth to a rich line of applications, many of which are imaging related (eg, [4-9]). Basically, a CS application seeks to capture only the essential signal components assuming that the signal could be sparsely represented in some arbitrary basis. When adopting the CS paradigm, we seek to minimize the collection of redundant data in the acquisition step, rather than in a post processing stage. Therefore, two or higher dimensional imaging is one of the most beneficial fields from the evolution of the CS theory [5], since almost any natural scene can be sparsely represented using wavelets, DCT or other sparsifying operators. Practically, the CS theory suggests that we can perfectly reconstruct a signal, by evoking an ℓ1 norm minimization procedure, with only M=O(KlogN) randomly selected projections …

publication date

  • January 1, 2011