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-en...

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Bibliographic Details
Published in:arXiv.org 2023-09
Main Authors: Isıl, Cagatay, Gan, Tianyi, Ardic, F Onuralp, Mentesoglu, Koray, Digani, Jagrit, Karaca, Huseyin, Chen, Hanlong, Li, Jingxi, Mengu, Deniz, Jarrahi, Mona, Akşit, Kaan, Ozcan, Aydogan
Format: Article
Language:English
Subjects:
Algorithms
Deep learning
Digital imaging
Display devices
Field of view
Graphics processing units
Inverse problems
Light speed
Microprocessors
Noise reduction
Power consumption
Power efficiency
Power management
Three dimensional printing
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Summary: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. 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.
ISSN:2331-8422
DOI:10.48550/arxiv.2309.09215