Deep Learning-Based Joint Reconstruction and System Optimization for Single-Shot Compressive Spectral Imaging

Date

2022-01-01

Author

Gundogan, Utku
Öktem, Sevinç Figen

Metadata

Show full item record

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Item Usage Stats

88
views

0
downloads

We develop a joint reconstruction and system optimization method for snapshot spectral imaging with diffractive lenses. The method learns the diffractive lens design parameters jointly with a 3D deep prior in an unrolled reconstruction. Results illustrate the significance of jointly optimizing the prior and design parameters.

URI

https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139172891&origin=inward
https://hdl.handle.net/11511/101744

Conference Name

Computational Optical Sensing and Imaging, COSI 2022

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

Suggestions

OpenMETU
Core

Model-based Inversion Methods for Compressive Spectral Imaging with Diffractive Lenses Dogan, Didem; Öktem, Sevinç Figen (null; 2020-06-22) We develop novel model-based inversion methods for compressive spectral imaging with diffractive lenses. These fast image reconstruction methods, exploiting data- adaptive convolutional dictionaries and sparsifying transforms, are applicable to any computational imaging problem with convolutional models.
Anlık Spektral Görüntüleme için Tasarım Eniyileme Ayazgök, Suleyman; Öktem, Sevinç Figen (2019-08-22) Snapshot spectral imaging enables to reconstructspectral images from a multiplexed single-shot measurement.Since an inversion is required to form the spectral images com-putationally, quantitative characterization of their performanceis essential to optimize the design. In this paper, we analyze theoptimal design of a snapshot spectral imaging technique. Thissnapshot multi-spectral imaging technique uses a diffractive lenscalled generalized photon sieve, and vari...
Spatial modulation of THz beams for imaging applications Altan, Hakan (null; 2015-10-16) Techniques based on compressive sensing allow us to image fields at faster rates and at our labs in METU we have been experimenting with imaging based on spatial modulation of THz beams using single pixel detectors [1]. However these studies are based on discrete patterns using metal sheets. These techniques would benefit greatly if we could modulate the THz field. For example, optical modulators play a key role in optoelectronics and communication systems. Electro-optic, acousto-optic and thermo-optic effe...
Compressive spectral imaging with diffractive lenses Kar, Oguzhan Fatih; Öktem, Sevinç Figen (The Optical Society, 2019-09-15) Compressive spectral imaging enables the reconstruction of an entire 3D spectral cube from a few multiplexed images. Here we develop a novel compressive spectral imaging technique using diffractive lenses. Our technique uses a coded aperture to spatially modulate the optical field from the scene and a diffractive lens such as a photon sieve for both dispersion and focusing. Measurement diversity is achieved by changing the focusing behavior of the diffractive lens. The 3D spectral cube is then reconstructed...
EFFICIENT SPARSITY-BASED INVERSION FOR PHOTON-SIEVE SPECTRAL IMAGERS WITH TRANSFORM LEARNING Kamaci, Ulas; Akyon, Fatih C.; Alkanat, Tunc; Öktem, Sevinç Figen (2017-01-01) We develop an efficient and adaptive sparse reconstruction approach for the recovery of spectral images from the measurements of a photon-sieve spectral imager (PSSI). PSSI is a computational imaging technique that enables higher resolution than conventional spectral imagers. Each measurement in PSSI is a superposition of the blurred spectral images; hence, the inverse problem can be viewed as a type of multi-frame deconvolution problem involving multiple objects. The transform learning-based approach recon...

Citation Formats

U. Gundogan and S. F. Öktem, “Deep Learning-Based Joint Reconstruction and System Optimization for Single-Shot Compressive Spectral Imaging,” presented at the Computational Optical Sensing and Imaging, COSI 2022, Vancouver, Kanada, 2022, Accessed: 00, 2023. [Online]. Available: https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85139172891&origin=inward.