All-optical image denoising using a diffractive visual processor
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2023
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866916113917411328 |
|---|---|
| author | 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 |
| author_facet | 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 |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_09215 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | All-optical image denoising using a diffractive visual processor 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 Optics Computer Vision and Pattern Recognition Applied Physics 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. |
| title | All-optical image denoising using a diffractive visual processor |
| topic | Optics Computer Vision and Pattern Recognition Applied Physics |
| url | https://arxiv.org/abs/2309.09215 |