Non-invasive single-shot recovery of a point-spread function of a memory effect based scattering imaging system

Super-resolution imaging through scattering media based on improved triple correlation recursion and deterministic iterative estimation

Iterative phase retrieval algorithms are commonly used in computational techniques and optimization methods to obtain the reconstruction of objects hidden behind opaque scattering media. However, these methods are susceptible to converging to incorrect local minima, and the calculation results tend to be unstable. In this paper, a triple-correlation-based super-resolution imaging (TCSI) framework is proposed to achieve single-shot imaging of unknown objects hidden behind the scattering medium. The amplitude spectrum of the object is obtained by a speckle correlation (SC) method. Iterative relaxation recursion (IRR) sufficiently extracts object information from the triple correlation (TC) of the speckle patterns, serving as the prior initial guess for the iterative estimation algorithm (IE) to obtain a deterministic phase spectrum. Blur correction (BC) is then applied to the diffraction-limited image to achieve super-resolution imaging. Experimental results demonstrate that the flexible framework could effectively overcome the influence of speckle resolution and outperform traditional methods in terms of performance. Our approach provides a basis for non-invasively visualizing various samples behind scattering media.

Imaging through a scattering medium via model-driven deep learning

Imaging through a scattering medium is of great significance in many areas. Especially, speckle correlation imaging has been valued for its noninvasiveness. In this work, we report a deep learning solution that incorporates the physical model and an additional regularization for high-fidelity speckle correlation imaging. Without large-scale data to train, the physical model and regularization prior provide a correct direction for neural network to precisely reconstruct hidden objects from speckle under different scattering scenarios and noise levels. Experimental results demonstrate that the proposed method presents a significant advance in improving generalization and combating the invasion of noise.

Deep speckle reassignment: towards bootstrapped imaging in complex scattering states with limited speckle grains

Optical imaging through scattering media is a practical challenge with crucial applications in many fields. Many computational imaging methods have been designed for object reconstruction through opaque scattering layers, and remarkable recovery results have been demonstrated in the physical models or learning models. However, most of the imaging approaches are dependent on relatively ideal states with a sufficient number of speckle grains and adequate data volume. Here, the in-depth information with limited speckle grains has been unearthed with speckle reassignment and a bootstrapped imaging method is proposed for reconstruction in complex scattering states. Benefiting from the bootstrap priors-informed data augmentation strategy with a limited training dataset, the validity of the physics-aware learning method has been demonstrated and the high-fidelity reconstruction results through unknown diffusers are obtained. This bootstrapped imaging method with limited speckle grains broadens the way to highly scalable imaging in complex scattering scenes and gives a heuristic reference to practical imaging problems.

Plug-and-play algorithm for imaging through scattering media under ambient light interference

Imaging through scattering media is a fascinating subject in the computational imaging domain. The methods based on speckle correlation imaging have found tremendous versatility. However, a darkroom condition without any stray light is required because the speckle contrast is easily disturbed by ambient light, which can lead to the reduction in object reconstruction quality. Here, we report a plug-and-play (PnP) algorithm to restore the object through scattering media under the non-darkroom environment. Specifically, the PnPGAP-FPR method is established via the generalized alternating projection (GAP) optimization framework, Fienup phase retrieval (FPR) method, and FFDNeT. The proposed algorithm is demonstrated experimentally and shows significant effectiveness and flexible scalability, which describe the potential for its practical applications.

Single-shot generalized Hanbury Brown-Twiss experiments using a polarization camera for target intensity reconstruction in scattering media

To see through a random light field in real-time, single-shot generalized Hanbury Brown-Twiss experiments using a polarization camera are proposed. The target intensity distribution is obtained from a complex coherence function which is calculated from auto-correlation and cross correlation functions of phase-shifted speckle intensity distributions. The phase-shifted speckle intensity distributions are simultaneously obtained through a strategy of parallel phase-shifting digital holography. Experimental results show that the proposed method can image a moving object in a random light field using a measured complex coherence function through the van Cittert-Zernike theorem.

Robust and adjustable dynamic scattering compensation for high-precision deep tissue optogenetics

The development of high-precision optogenetics in deep tissue is limited due to the strong optical scattering induced by biological tissue. Although various wavefront shaping techniques have been developed to compensate the scattering, it is still a challenge to non-invasively characterize the dynamic scattered optical wavefront inside the living tissue. Here, we present a non-invasive scattering compensation system with fast multidither coherent optical adaptive technique (fCOAT), which allows the rapid wavefront correction and stable focusing in dynamic scattering medium. We achieve subcellular-resolution focusing through 500-μm-thickness brain slices, or even three pieces overlapped mouse skulls after just one iteration with a 589 nm CW laser. Further, focusing through dynamic scattering medium such as live rat ear is also successfully achieved. The formed focus can maintain longer than 60 s, which satisfies the requirements of stable optogenetics manipulation. Moreover, the focus size is adjustable from subcellular level to tens of microns to freely match the various manipulation targets. With the specially designed fCOAT system, we successfully achieve single-cellular optogenetic manipulation through the brain tissue, with a stimulation efficiency enhancement up to 300% compared with that of the speckle.

Prior-free imaging unknown target through unknown scattering medium

Imaging through scattering medium based on deep learning has been extensively studied. However, existing methods mainly utilize paired data-prior and lack physical-process fusion, and it is difficult to reconstruct hidden targets without the trained networks. This paper proposes an unsupervised neural network that integrates the universal physical process. The reconstruction process of the network is irrelevant to the system and only requires one frame speckle pattern and unpaired targets. The proposed network enables online optimization by using physical process instead of fitting data. Thus, large-scale paired data no longer need to be obtained to train the network in advance, and the proposed method does not need prior information. The optimization of the network is a physical-based process rather than a data mapping process, and the proposed method also increases the insufficient generalization ability of the learning-based method in scattering medium and targets. The universal applicability of the proposed method to different optical systems increases the likelihood that the method will be used in practice.

Single-shot noninvasive imaging through scattering medium under white-light illumination

We experimentally investigate image reconstruction through a scattering medium under white-light illumination. To solve the inverse problem of noninvasive scattering imaging, a modified iterative algorithm is employed with an interpretable constraint on the optical transfer function (OTF). As a result, a sparse and real object can be retrieved whether it is illuminated with a narrowband or broadband light. Compared with the well-known speckle correlation technique (SCT), the proposed method requires no restrictions on the speckle autocorrelation and shows a potential advantage in scattering imaging.

Deconvolved Image Restoration From Auto-Correlations

The recovery of a real signal from its auto-correlation is a wide-spread problem in computational imaging, and it is equivalent to retrieve the phase linked to a given Fourier modulus. Image-deconvolution, on the other hand, is a funda- mental aspect to take into account when we aim at increasing the resolution of blurred signals. These problems are addressed separately in a large number of experimental situations, ranging from adaptive astronomy to optical microscopy. Here, instead, we tackle both at the same time, performing auto-correlation inversion while deconvolving the current object estimation. To this end, we propose a method based on I -divergence optimization, turning our formalism into an iterative scheme inspired by Bayesian-based approaches. We demonstrate the method by recovering sharp signals from blurred auto-correlations, regardless of whether the blurring acts in auto-correlation, object, or Fourier domain.