All-optical image denoising using a diffractive visual processor

• 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: Journal Article
• Language: English
• Published: 17-09-2023
• Subjects: Computer Science - Computer Vision and Pattern Recognition; Physics - Applied Physics; Physics - Optics
• DOI: 10.48550/arxiv.2309.09215

Light: Science & Applications (2024) 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.