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Register a new userInverse problem solver for multiple light scattering using modified Born series
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The inverse scattering problem, whose goal is to reconstruct an unknown scattering object from its scattered wave, is essential in fundamental wave physics and its wide applications in imaging sciences. However, it remains challenging to invert multiple scattering accurately and efficiently. Here, we exploit the modified Born series to demonstrate an inverse problem solver that efficiently and directly computes inverse multiple scattering without making any assumptions. The inversion process is based on a physically intuitive approach and can be easily extended to other exact forward solvers. We utilised the proposed method in optical diffraction tomography and numerically and experimentally demonstrated three-dimensional reconstruction of optically thick specimens with higher fidelity than those obtained using conventional methods based on the weak scattering approximation.
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Exciting optical effects such as polarization control, imaging, and holography were demonstrated at the nanoscale using the complex and irregular structures of nanoparticles with the multipole Mie-resonances in the optical range. The optical response of such particles can be simulated either by full wave numerical simulations or by the widely used analytical coupled multipole method (CMM), however, an analytical solution in the framework of CMM can be obtained only in a limited number of cases. In this paper, a modification of the CMM in the framework of the Born series and its applicability for simulation of light scattering by finite nanosphere structures, maintaining both dipole and quadrupole resonances, are investigated. The Born approximation simplifies an analytical consideration of various systems and helps shed light on physical processes ongoing in that systems. Using Mie theory and Greens functions approach, we analytically formulate the rigorous coupled dipole-quadrupole equations and their solution in the different-order Born approximations. We analyze in detail the resonant scattering by dielectric nanosphere structures such as dimer and ring to obtain the convergence conditions of the Born series and investigate how the physical characteristics such as absorption in particles, type of multipole resonance, and geometry of ensemble influence the convergence of Born series and its accuracy.
We present an efficient, effective, and generic approach towards solving inverse problems. The key idea is to leverage the feedback signal provided by the forward process and learn an iterative update model. Specifically, at each iteration, the neural network takes the feedback as input and outputs an update on the current estimation. Our approach does not have any restrictions on the forward process; it does not require any prior knowledge either. Through the feedback information, our model not only can produce accurate estimations that are coherent to the input observation but also is capable of recovering from early incorrect predictions. We verify the performance of our approach over a wide range of inverse problems, including 6-DOF pose estimation, illumination estimation, as well as inverse kinematics. Comparing to traditional optimization-based methods, we can achieve comparable or better performance while being two to three orders of magnitude faster. Compared to deep learning-based approaches, our model consistently improves the performance on all metrics. Please refer to the project page for videos, animations, supplementary materials, etc.
Light scattering is one of the most important elementary processes in near-field optics. We build up the Born series for scattering by dielectric bodies with step boundaries. The Green function for a 2-dimensional homogeneous dielectric cylinder is obtained. As an example, the formulas are derived for scattered field of two parallel cylinders. The polar diagram is shown to agree with numerical calculation by the known methods of discrete dipoles and boundary elements.
The efficient delivery of light energy is a prerequisite for non-invasive imaging and stimulating of target objects embedded deep within a scattering medium. However, injected waves experience random diffusion by multiple light scattering, and only a small fraction reaches the target object. Here we present a method to counteract wave diffusion and to focus multiplescattered waves to the deeply embedded target. To realize this, we experimentally inject light to the reflection eigenchannels of a specific flight time where most of the multiple-scattered waves have interacted with the target object and maximize the intensity of the returning multiple-scattered waves at the selected time. For targets that are too deep to be visible by optical imaging, we demonstrated a more than 10-fold enhancement in light energy delivery in comparison with ordinary wave diffusion cases. This work will lay a foundation for enhancing the working depth of imaging, sensing, and light stimulation.
Our everyday experience teaches us that the structure of a medium strongly influences how light propagates through it. A disordered medium, e.g., appears transparent or opaque, depending on whether its structure features a mean free path that is larger or smaller than the medium thickness. While the microstructure of the medium uniquely determines the shape of all penetrating light paths, recent theoretical insights indicate that the mean length of these paths is entirely independent of any structural medium property and thus also invariant with respect to a change in the mean free path. Here, we report an experiment that demonstrates this surprising property explicitly. Using colloidal solutions with varying concentration and particle size, we establish an invariance of the mean path length spanning nearly two orders of magnitude in scattering strength, from almost transparent to very opaque media. This very general, fundamental and counterintuitive result can be extended to a wide range of systems, however ordered, correlated or disordered, and has important consequences for many fields, including light trapping and harvesting for solar cells and more generally in photonic structure design.
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