All-optical image denoising using a diffractive visual processor  

作　　者：Çağatay Işıl Tianyi Gan Fazil Onuralp Ardic Koray Mentesoglu Jagrit Digani Huseyin Karaca Hanlong Chen Jingxi Li Deniz Mengu Mona Jarrahi Kaan Akşit Aydogan Ozcan 

机构地区：[1]Electrical and Computer Engineering Department,University of California,Los Angeles,CA 90095,USA [2]Bioengineering Department,University of California,Los Angeles,CA 90095,USA [3]California NanoSystems Institute(CNSI),University of California,Los Angeles,CA 90095,USA [4]University College London,Department of Computer Science,London,United Kingdom

出　　处：《Light(Science & Applications)》2024年第3期429-445,共17页光（科学与应用)（英文版）

基　　金：Research Group at UCLA acknowledges the support of U.S.Department of Energy(DOE),Office of Basic Energy Sciences,Division of Materials Sciences and Engineering under Award#DE-SC0023088.

摘　　要：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.

关 键 词：REMOVE RENDERING HOLOGRAPHIC 

分 类 号：O43[机械工程—光学工程]