홈 › Papers

Comparison between Hadamard and canonical bases for in-situ wavefront correction and the effect of ordering in compressive sensing

In this work we compare the Canonical and Hadamard bases for in-situ wavefront correction of a focused Gaussian beam using a spatial light modulator (SLM). The beam is perturbed with a transparent optical element (sparse) or a random scatterer (both prevent focusing at a single spot). The phase corrections are implemented with different basis sizes ($N=64, 256, 1024, 4096$) and the phase contribution of each basis element is measured with 3 step interferometry. The field is reconstructed from the complete $3N$ measurements and the correction is implemented by projecting the conjugate phase at the SLM. Our experiments show that in general, the Hadamard basis measurements yield better corrections because every element spans the relevant area of the SLM, reducing the noise in the interferograms. % In contrast, the canonical basis has the fundamental limitation that the area of the elements is proportional to $1/N$, and requires dimensions that are compatible with the spatial period of the grating. In the case of the random scatterer, we were only able to get reasonable corrections with the Hadamard basis and the intensity of the corrected spot increased monotonically with $N$, which is consistent with fast random changes in phase over small spatial scales. We also explore compressive sensing with the Hadamard basis and find that the minimum compression ratio needed to achieve corrections with similar quality to those that use the complete measurements depend on the basis ordering. The best results are reached in the case of the Hadamard-Walsh and cake cutting orderings. Surprisingly, in the case of the random scatterer we find that moderate compression ratios on the order of $10-20\%$ ($N=4096$) allow to recover focused spots, although as expected, the maximum intensities increase monotonically with the number of measurements due to the non sparsity of the signal.

Code (0)

등록된 구현이 없습니다.

Tasks

Methods 이 논문이 사용한 방법론

Similar Papers 제목 키워드 기반