SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Reed, Albert; Blanford, Thomas E.; Brown, Daniel C.; Jayasuriya, Suren

doi:10.1109/jstsp.2022.3215849

Cited by 5 publications

(6 citation statements)

References 56 publications

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“…Several works consider apply neural fields for sonar and SAS image reconstruction. Reed et al leverage neural fields to perform 2D CSAS deconvolution [Reed et al 2021a[Reed et al , 2022. Their method post-processes (deblurs) reconstructed 2D scenes for circular SAS measurement geometries [Reed et al 2022].…”

Section: Neural Fieldsmentioning

confidence: 99%

“…Reed et al leverage neural fields to perform 2D CSAS deconvolution [Reed et al 2021a[Reed et al , 2022. Their method post-processes (deblurs) reconstructed 2D scenes for circular SAS measurement geometries [Reed et al 2022]. On the other hand, our proposed approach focuses on reconstruction for 3D SAS.…”

Section: Neural Fieldsmentioning

confidence: 99%

“…Importantly, the relevant sound physics between air and water are directly analogous for the purposes of this work. AirSAS data has been used extensively in prior literature for proof-of-concept demonstrations Cowen et al 2021;Goehle et al 2022;Park et al 2020;Reed et al 2022].…”

Section: Airsasmentioning

confidence: 99%

“…and Anderson 2005]. Second, SAS transducers (transmitter and receiver) are bandlimited, limiting the ability to reconstruct arbitrarily fine spatial frequencies in imagery [de Heering et al 1994;Pailhas et al 2017;Reed et al 2022].…”

Section: Introductionmentioning

confidence: 99%

“…While post-processing optimizations are used (e.g. image-space deconvolution [de Heering et al 1994;Pailhas et al 2017;Reed et al 2022], autofocus [Fienup 2000;Gerg and Monga 2020;Mansour et al 2018;), these methods seek to improve already reconstructed SAS images, rather than incorporate the physics and priors into the image formation.…”

Section: Introductionmentioning

confidence: 99%

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Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

et al. 2023

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Synthetic aperture sonar (SAS) measures a scene from multiple views in order to increase the resolution of reconstructed imagery. Image reconstruction methods for SAS coherently combine measurements to focus acoustic energy onto the scene. However, image formation is typically under-constrained due to a limited number of measurements and bandlimited hardware, which limits the capabilities of existing reconstruction methods. To help meet these challenges, we design an analysis-by-synthesis optimization that leverages recent advances in neural rendering to perform coherent SAS imaging. Our optimization enables us to incorporate physics-based constraints and scene priors into the image formation process. We validate our method on simulation and experimental results captured in both air and water. We demonstrate both quantitatively and qualitatively that our method typically produces superior reconstructions than existing approaches. We share code and data for reproducibility.

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Section: Neural Fieldsmentioning

confidence: 99%