Publications

2023

1. Imaging Congress

   

   Shift-variant PSF characterization of DOE-based imaging systems via implicit neural representations

   David Morales-Norato, Henry Arguello, and Hoover Rueda-Chacón

   In Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP) 2023

   Abs Bib HTML

   This work proposes to learn implicit neural representations of shift-variant PSFs for optical imaging systems that use diffractive optical elements. Results show that the proposed method is able to characterize shift-variant PSFs from sparse samples.

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     abbr = {Imaging Congress},
     author = {Morales-Norato, David and Arguello, Henry and Rueda-Chac\'{o}n, Hoover},
     booktitle = {Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},
     journal = {Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},
     keywords = {Computational imaging; Diffractive optical elements; Imaging systems; Optical aberration; Optical imaging; Optical systems},
     pages = {CW3B.5},
     publisher = {Optica Publishing Group},
     title = {Shift-variant PSF characterization of DOE-based imaging systems via implicit neural representations},
     year = {2023},
     url = {https://opg.optica.org/abstract.cfm?URI=COSI-2023-CW3B.5},
     html = {https://opg.optica.org/abstract.cfm?URI=COSI-2023-CW3B.5},
     doi = {10.1364/COSI.2023.CW3B.5},
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2. Imaging Congress

   

   Multi-domain Discrete Spectral Image Fusion via Implicit Neural Representation

   Manuel Herrera-Poveda, David Morales-Norato, Hans Garcia, and 3 more authors

   In Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP) 2023

   Abs Bib HTML

   This work proposes a spectral image fusion methodology by using implicit neural representation, which allows obtaining continuous spectral information from multi-domain discrete measurements.

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     abbr = {Imaging Congress},
     author = {Herrera-Poveda, Manuel and Morales-Norato, David and Garcia, Hans and Castillo, Sergio and Jerez, Andr\'{e}s and Arguello, Henry},
     booktitle = {Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},
     journal = {Optica Imaging Congress (3D, COSI, DH, FLatOptics, IS, pcAOP)},
     keywords = {Hyperspectral imaging; Imaging techniques; Neural networks; Optical systems; Quantum efficiency; Sensors},
     pages = {CTh2A.3},
     publisher = {Optica Publishing Group},
     title = {Multi-domain Discrete Spectral Image Fusion via Implicit Neural Representation},
     year = {2023},
     url = {https://opg.optica.org/abstract.cfm?URI=COSI-2023-CTh2A.3},
     html = {https://opg.optica.org/abstract.cfm?URI=COSI-2023-CTh2A.3},
     doi = {10.1364/COSI.2023.CTh2A.3},
     preview = {multidomain_fusion.png},
     selected = {true}
   }

3. Photonics North

   

   Compressive Spectral Video via Wavelength-Dynamic Coded Aperture

   David Morales-Norato, M. Marquez, A. Jerez, and 2 more authors

   In 2023 Photonics North (PN) 2023

   Bib HTML

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     abbr = {Photonics North},
     author = {Morales-Norato, David and Marquez, M. and Jerez, A. and Arguello, H. and Meza, P.},
     booktitle = {2023 Photonics North (PN)},
     title = {Compressive Spectral Video via Wavelength-Dynamic Coded Aperture},
     year = {2023},
     volume = {},
     number = {},
     pages = {1-2},
     doi = {10.1109/PN58661.2023.10221940},
     html = {https://ieeexplore.ieee.org/abstract/document/10221940?casa_token=aF_iwTaSXXIAAAAA:eE0IzAox9hzr2aocrpCbVYneA8mPBx25k86nVtsOurM5YAB5NNKy_Xid8GIIONqR8CZBtWtdh_u0}
   }

4. Applied Optics

   

   Hyperspectral camera as a compact payload architecture for remote sensing applications

   David Morales-Norato, Sergio Urrea, Hans Garcia, and 9 more authors

   Applied Optics 2023

   Abs Bib HTML

   Monitoring and observation over the surface of the Earth have been a matter of global interest. In this path, recent efforts aim to develop a spatial mission to perform remote sensing applications. Mainly, CubeSat nanosatellites have emerged as a standard for developing low-weight and small-sized instruments. In terms of payloads, state-of-the-art optical systems for CubeSats are expensive and designed to work in general use cases. To overcome these limitations, this paper presents a 1.4 U compact optical system to acquire spectral images from a CubeSat standard satellite at the height of 550 km. To validate the proposed architecture, optical simulations using ray tracing simulation software are presented. Because the performance of computer vision tasks is highly related to data quality, we compared the optical system in terms of the classification performance on a real remote sensing application. The performances of the optical characterization and land cover classification show that the proposed optical system achieves a compact instrument, operating at a spectral range from 450 nm to 900 nm discretized on 35 spectral bands. The optical system has an overall f-number of 3.41 with a ground sampling distance of 52.8 m and a swath of 40 km. Additionally, the design parameters for each optical element are publicly available for validation, repeatability, and reproducibility of the results.

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     abbr = {Applied Optics},
     author = {Morales-Norato, David and Urrea, Sergio and Garcia, Hans and Rodriguez-Ferreira, Julian and Martinez, Elizabeth and Arguello, Henry and Silva-Lora, Alberto and Torres, Rafael and Acero, Ignacio F. and Hern\'{a}ndez, Francisco L. and C\'{a}rdenas, Lorena P. and Rinc\'{o}n, Sonia},
     journal = {Applied Optics},
     keywords = {Imaging systems; Machine vision; Optical elements; Optical systems; Ray tracing; Systems design},
     number = {8},
     pages = {C88--C98},
     publisher = {Optica Publishing Group},
     title = {Hyperspectral camera as a compact payload architecture for remote sensing applications},
     volume = {62},
     month = mar,
     year = {2023},
     url = {https://opg.optica.org/ao/abstract.cfm?URI=ao-62-8-C88},
     html = {https://opg.optica.org/ao/abstract.cfm?URI=ao-62-8-C88},
     doi = {10.1364/AO.476978},
     selected = {true}
   }

2022

1. Applied Optics

   

   Learning spectral initialization for phase retrieval via deep neural networks

   David Morales-Norato, Andrés Jerez, and Henry Arguello

   Applied Optics 2022

   Abs HTML

   Phase retrieval (PR) arises from the lack of phase information in the measures recorded by optical sensors. Phase masks that modulate the optical field and reduce ambiguities in the PR problem by producing redundancy in coded diffraction patterns (CDPs) have been included in these diffractive optical systems. Several algorithms have been developed to solve the PR problem from CDPs. Also, deep neural networks (DNNs) are used for solving inverse problems in computational imaging by considering physical constraints in propagation models. However, traditional algorithms based on non-convex formulation include an initialization stage that requires a high number of iterations to properly estimate the optical field. This work proposes an end-to-end (E2E) approach for addressing the PR problem, which jointly learns the spectral initialization and network parameters. Mainly, the proposed deep network approach contains an optical layer that simulates the propagation model in diffractive optical systems, an initialization layer that approximates the underlying optical field from CDPs, and a double branch DNN that improves the obtained initial guess by separately recovering phase and amplitude information. Simulation results show that the proposed E2E approach for PR requires fewer snapshots and iterations than the state of the art.

2021

1. STSIVA

   

   Object Classification using Deep Neural Networks from Coded Diffraction Patterns

   David Morales-Norato, Andrés Jerez, and Henry Arguello

   In 2021 XXIII Symposium on Image, Signal Processing and Artificial Vision (STSIVA) 2021

   Bib

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     abbr = {STSIVA},
     author = {Morales-Norato, David and Jerez, Andrés and Arguello, Henry},
     booktitle = {2021 XXIII Symposium on Image, Signal Processing and Artificial Vision (STSIVA)},
     title = {Object Classification using Deep Neural Networks from Coded Diffraction Patterns},
     year = {2021},
     volume = {},
     number = {},
     pages = {1-5},
     doi = {10.1109/STSIVA53688.2021.9591996}
   }

2. Imaging Congress

   

   Deep Phase Retrieval by a Learnable Filtered Spectral Initialization

   David Morales-Norato, Andrés Jerez, and Henry Arguello

   In OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP) 2021

   Abs Bib HTML

   This work proposes a deep neural network approach for the phase retrieval problem, which learns a filtered spectral initialization. Simulation results show that the proposed approach requires fewer snapshots and power iterations than state-of-the-art.

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     author = {Morales-Norato, David and Jerez, Andr\'{e}s and Arguello, Henry},
     booktitle = {OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)},
     journal = {OSA Imaging and Applied Optics Congress 2021 (3D, COSI, DH, ISA, pcAOP)},
     keywords = {Fourier transforms; Neural networks; Optical imaging; Optical systems; Phase retrieval; Signal processing},
     pages = {CTh4A.6},
     publisher = {Optica Publishing Group},
     title = {Deep Phase Retrieval by a Learnable Filtered Spectral Initialization},
     year = {2021},
     url = {https://opg.optica.org/abstract.cfm?URI=COSI-2021-CTh4A.6},
     html = {https://opg.optica.org/abstract.cfm?URI=COSI-2021-CTh4A.6},
     doi = {10.1364/COSI.2021.CTh4A.6},
     selected = {true}
   }

2020

1. ColCACI

   

   Snapshot compressive spectral video via a monocular optical system

   David Morales-Norato, Paula Arguello, Miguel Márquez, and 1 more author

   In 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) 2020

   Bib

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     bibtex_show = {true},
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     abbr = {ColCACI},
     author = {Morales-Norato, David and Arguello, Paula and Márquez, Miguel and Arguello, Henry},
     booktitle = {2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)},
     title = {Snapshot compressive spectral video via a monocular optical system},
     year = {2020},
     volume = {},
     number = {},
     pages = {1-6},
     doi = {10.1109/ColCACI50549.2020.9248717}
   }

2. ColCACI

   

   Video-Tensor Completion using a Deep Learning approach

   Paula Arguello, David Morales-Norato, Yesid Fonseca, and 1 more author

   In 2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020) 2020

   Bib

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     abbr = {ColCACI},
     author = {Arguello, Paula and Morales-Norato, David and Fonseca, Yesid and Arguello, Henry},
     booktitle = {2020 IEEE Colombian Conference on Applications of Computational Intelligence (IEEE ColCACI 2020)},
     title = {Video-Tensor Completion using a Deep Learning approach},
     year = {2020},
     volume = {},
     number = {},
     pages = {1-6},
     doi = {10.1109/ColCACI50549.2020.9247929}
   }