All-optical image denoising using a diffractive visual processor

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

doi:10.1038/s41377-024-01385-6

All-optical image denoising using a diffractive visual processor

Light Sci Appl. 2024 Feb 4;13(1):43. doi: 10.1038/s41377-024-01385-6.

Authors

Çağatay Işıl^{1

2

3}, Tianyi Gan^{1

3}, Fazil Onuralp Ardic¹, Koray Mentesoglu¹, Jagrit Digani¹, Huseyin Karaca¹, Hanlong Chen^{1

2

3}, Jingxi Li^{1

2

3}, Deniz Mengu^{1

2

3}, Mona Jarrahi^{1

3}, Kaan Akşit⁴, Aydogan Ozcan^{5

6

7}

Affiliations

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.
⁵ Electrical and Computer Engineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.
⁶ Bioengineering Department, University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.
⁷ California NanoSystems Institute (CNSI), University of California, Los Angeles, CA, 90095, USA. ozcan@ucla.edu.

Abstract

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, where λ is 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.

Grants and funding

DE-SC0023088/U.S. Department of Energy (DOE)