Unidirectional imaging with partially coherent light

.Unidirectional imagers form images of input objects only in one direction,e.g.,from field-of-view(FOV)A to FOV B,while blocking the image formation in the reverse direction,from FOV B to FOV A.Here,we report unidirectional imaging under spatially partially coherent light and demonstrate high-quality imaging only in the forward direction(A→B)with high power efficiency while distorting the image formation in the backward direction(B→A)along with low power efficiency.Our reciprocal design features a set of spatially engineered linear diffractive layers that are statistically optimized for partially coherent illumination with a given phase correlation length.Our analyses reveal that when illuminated by a partially coherent beam with a correlation length of≥∼1.5λ,whereλis the wavelength of light,diffractive unidirectional imagers achieve robust performance,exhibiting asymmetric imaging performance between the forward and backward directions—as desired.A partially coherent unidirectional imager designed with a smaller correlation length of<1.5λstill supports unidirectional image transmission but with a reduced figure of merit.These partially coherent diffractive unidirectional imagers are compact(axially spanning<75λ),polarization-independent,and compatible with various types of illumination sources,making them well-suited for applications in asymmetric visual information processing and communication.

All-optical image denoising using a diffractive visual processor

Image denoising,one of the essential inverse problems,targets to remove noise/artifacts from input images.In general,digital image denoising algorithms,executed on computers,present latency due to several iterations implemented in,e.g.,graphics processing units(GPUs).While deep learning-enabled methods can operate non-iteratively,they also introduce latency and impose a significant computational burden,leading to increased power consumption.Here,we introduce an analog diffractive image denoiser to all-optically and non-iteratively clean various forms of noise and artifacts from input images–implemented at the speed of light propagation within a thin diffractive visual processor that axially spans<250×λ,whereλis the wavelength of light.This all-optical image denoiser comprises passive transmissive layers optimized using deep learning to physically scatter the optical modes that represent various noise features,causing them to miss the output image Field-of-View(FoV)while retaining the object features of interest.Our results show that these diffractive denoisers can efficiently remove salt and pepper noise and image rendering-related spatial artifacts from input phase or intensity images while achieving an output power efficiency of~30–40%.We experimentally demonstrated the effectiveness of this analog denoiser architecture using a 3D-printed diffractive visual processor operating at the terahertz spectrum.Owing to their speed,power-efficiency,and minimal computational overhead,all-optical diffractive denoisers can be transformative for various image display and projection systems,including,e.g.,holographic displays.