Reconstructing targets based on the enhancement of speckle patterns with the hybrid input-output algorithm

Abstract

Recovering the object hidden in the disorganized speckle pattern generated through diffusive materials is an important topic as well as a difficult challenge. Existing speckle correlation imaging approaches generally use the speckle autocorrelation to extract the Fourier amplitude information of the target. Our goal here is to research the effects of the quality of the speckle autocorrelation on reconstructing targets via HIO-ER (hybrid input-output and the error reduction) algorithm. Specifically, a low-quality speckle pattern is preprocessed to estimate a high-quality autocorrelation. The PSNR of preprocessed autocorrelations could be increased from 5.88 dB to 24.08 dB. We also compare the differences between the preprocessed and unprocessed methods, and the reconstruction quality could be significantly improved than the later one. The result indicates that a high-quality speckle autocorrelation obtained after preprocessing helps to optimize reconstructing targets