Implicit Neural Representations for Deconvolving SAS Images

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

doi:10.23919/oceans44145.2021.9705799

Cited by 9 publications

(7 citation statements)

References 14 publications

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“…This work is an extension of an earlier conference paper [29]. We have extended the paper's results by implementing new methods, theory, and analysis to perform CSAS deconvolution, including handling the spatially-varying phase of the CSAS PSF.…”

Section: Introductionmentioning

confidence: 89%

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Reed¹,

Blanford²,

Brown³

et al. 2022

Preprint

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Circular Synthetic aperture sonars (CSAS) capture multiple observations of a scene to reconstruct high-resolution images. We can characterize resolution by modeling CSAS imaging as the convolution between a scene's underlying point scattering distribution and a system-dependent point spread function (PSF). The PSF is a function of the transmitted waveform's bandwidth and determines a fixed degree of blurring on reconstructed imagery. In theory, deconvolution overcomes bandwidth limitations by reversing the PSF-induced blur and recovering the scene's scattering distribution. However, deconvolution is an illposed inverse problem and sensitive to noise. We propose a selfsupervised pipeline (does not require training data) that leverages an implicit neural representation (INR) for deconvolving CSAS images. We highlight the performance of our SAS INR pipeline, which we call SINR, by implementing and comparing to existing deconvolution methods. Additionally, prior SAS deconvolution methods assume a spatially-invariant PSF, which we demonstrate yields subpar performance in practice. We provide theory and methods to account for a spatially-varying CSAS PSF, and demonstrate that doing so enables SINR to achieve superior deconvolution performance on simulated and real acoustic SAS data. We provide code 1 to encourage reproducibility of research.

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Section: Introductionmentioning

confidence: 89%

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Reed¹,

Blanford²,

Brown³

et al. 2022

Preprint

Self Cite

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“…Synthetic aperture sonar (SAS) also exhibits the common problems in inverse problems: noisy sensor domain and ill‐posed problem. Reed et al [RBBJ] use a neural field mapping a 2D position to a distribution of point scatter (reconstruction domain), and obtain supervision by mapping to the sensor domain via convolution.…”

Section: Beyond Visual Computingmentioning

confidence: 99%

“…Most of the works surveyed so far have been concerned with modeling the imaging process of consumer cameras, which measure the visible electromagnetic radiation via optical lenses, using sensors that digitize irradiance into intensity over a 2D raster grid. Nonetheless, neural fields can also model alternative signal modalities such as non‐line‐of‐sight imaging [SWL*21], non‐visible x‐rays for computed tomography (CT) [SPX21, ZIL*21, SLX*21], magnetic resonance imaging (MRI) [SPX21], pressure waves for audio [RBBJ], chemiluminescence [PXZ*21], time‐of‐flight imaging [ALG*21], as well as volumetric light displays [ZBW*20].…”

Section: Beyond Visual Computingmentioning

confidence: 99%

Neural Fields in Visual Computing and Beyond

Xie

Takikawa

Saito

et al. 2022

Computer Graphics Forum

231

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Manipulation 2D and 3D Reconstruction Generative Models Digital Humans Compression 903.63 KB Robotics …and Beyond! Figure 1: Contribution of this report. Following a survey of over 250 papers, we provide a review of (Part I) techniques in neural fields such as prior learning and conditioning, representations, forward maps, architectures, and manipulation, and of (Part II) applications in visual computing including 2D image processing, 3D scene reconstruction, generative modeling, digital humans, compression, robotics, and beyond. This report is complemented by a community-driven website with search, filtering, bibliographic, and visualization features.

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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%

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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Implicit Neural Representations for Deconvolving SAS Images

Cited by 9 publications

References 14 publications

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

SINR: Deconvolving Circular SAS Images Using Implicit Neural Representations

Neural Fields in Visual Computing and Beyond

Neural Volumetric Reconstruction for Coherent Synthetic Aperture Sonar

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