Maximum entropy method for imaging through turbid medium

Mingwei Huang; Zijing Zhang; Yuan Zhao

doi:10.1117/12.2587811

12 March 2021 Maximum entropy method for imaging through turbid medium

Mingwei Huang, Zijing Zhang, Yuan Zhao

Proceedings Volume 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications; 117639K (2021) https://doi.org/10.1117/12.2587811
Event: Seventh Symposium on Novel Photoelectronic Detection Technology and Application 2020, 2020, Kunming, China

Abstract

Imaging through turbid medium has many potential applications such as looking through clouds, seeing into seawater and observing through biological tissues. The transmission matrix (TM) method is one of the main imaging technologies that has potential in imaging of large targets. With aid of pre-measured TM, several optimization models are proposed to recover targets from speckle patterns, including ℓ₂ norm optimization model, sparse representation (SR) framework and total variation (TV) model. However, the solution of ℓ₂ norm optimization model contains large reconstruction noise, while the SR framework and TV model are two kinds of compressive sensing strategies, which require that the targets are sparse. In this paper, in order to image non-sparse targets and suppress the reconstruction noise, we apply the maximum entropy method (MEM) model to recover the target images from speckle patterns. Simulation results show that, for non-sparse target, the MEM model has better reconstruction performance under different noise levels compared with the TV model. For example, peak signal-to-noise ratio (PSNR) and correlation coefficient (CC) of images reconstructed by MEM model at SNR=15 dB are comparable with those by TV model at SNR=35 dB.

Citation Download Citation

Mingwei Huang, Zijing Zhang, and Yuan Zhao "Maximum entropy method for imaging through turbid medium", Proc. SPIE 11763, Seventh Symposium on Novel Photoelectronic Detection Technology and Applications, 117639K (12 March 2021); https://doi.org/10.1117/12.2587811

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