3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging

• URL: http://arxiv.org/abs/2402.06063v1
• Date: Thu, 8 Feb 2024 21:19:16 GMT
• Title: 3D-2D Neural Nets for Phase Retrieval in Noisy Interferometric Imaging
• Authors: Andrew H. Proppe, Guillaume Thekkadath, Duncan England, Philip J. Bustard, Fr\'ed\'eric Bouchard, Jeff S. Lundeen, Benjamin J. Sussman
• Abstract summary: We introduce a 3D-2D Phase Retrieval U-Net (PRUNe) that takes noisy and randomly phase-shifted interferograms as inputs, and outputs a single 2D phase image. A 3D downsampling convolutional encoder captures correlations within and between frames to produce a 2D latent space, which is upsampled by a 2D decoder into a phase image. We find PRUNe reconstructions consistently show more accurate and smooth reconstructions, with a x2.5 - 4 lower mean squared error at multiple signal-to-noise ratios for interferograms with low (
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In recent years, neural networks have been used to solve phase retrieval problems in imaging with superior accuracy and speed than traditional techniques, especially in the presence of noise. However, in the context of interferometric imaging, phase noise has been largely unaddressed by existing neural network architectures. Such noise arises naturally in an interferometer due to mechanical instabilities or atmospheric turbulence, limiting measurement acquisition times and posing a challenge in scenarios with limited light intensity, such as remote sensing. Here, we introduce a 3D-2D Phase Retrieval U-Net (PRUNe) that takes noisy and randomly phase-shifted interferograms as inputs, and outputs a single 2D phase image. A 3D downsampling convolutional encoder captures correlations within and between frames to produce a 2D latent space, which is upsampled by a 2D decoder into a phase image. We test our model against a state-of-the-art singular value decomposition algorithm and find PRUNe reconstructions consistently show more accurate and smooth reconstructions, with a x2.5 - 4 lower mean squared error at multiple signal-to-noise ratios for interferograms with low (< 1 photon/pixel) and high (~100 photons/pixel) signal intensity. Our model presents a faster and more accurate approach to perform phase retrieval in extremely low light intensity interferometry in presence of phase noise, and will find application in other multi-frame noisy imaging techniques.

Related papers

• Interferometry with few photons [0.0]
  We experimentally reconstruct different wavefronts by means of a point diffraction interferometer for different mean intensities of illumination. For two cases of interest, a spatial qudit encoding phase, consisting of d = 6 uniform phase regions, and a more general continuous phase, we see that reducing the readout noise leads to a clear improvement in the quality of reconstruction.
  arXiv  Detail & Related papers  (2024-02-20T17:21:06Z)
• StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D [88.66678730537777]
  We present StableDreamer, a methodology incorporating three advances. First, we formalize the equivalence of the SDS generative prior and a simple supervised L2 reconstruction loss. Second, our analysis shows that while image-space diffusion contributes to geometric precision, latent-space diffusion is crucial for vivid color rendition.
  arXiv  Detail & Related papers  (2023-12-02T02:27:58Z)
• A Deep Learning Method for Simultaneous Denoising and Missing Wedge Reconstruction in Cryogenic Electron Tomography [23.75819355889607]
  We propose a deep-learning approach for simultaneous denoising and missing wedge reconstruction called DeepDeWedge. The algorithm requires no ground truth data and is based on fitting a neural network to the 2D projections using a self-supervised loss.
  arXiv  Detail & Related papers  (2023-11-09T17:34:57Z)
• Towards High-quality HDR Deghosting with Conditional Diffusion Models [88.83729417524823]
  High Dynamic Range (LDR) images can be recovered from several Low Dynamic Range (LDR) images by existing Deep Neural Networks (DNNs) techniques. DNNs still generate ghosting artifacts when LDR images have saturation and large motion. We formulate the HDR deghosting problem as an image generation that leverages LDR features as the diffusion model's condition.
  arXiv  Detail & Related papers  (2023-11-02T01:53:55Z)
• Mutual Information-driven Triple Interaction Network for Efficient Image Dehazing [54.168567276280505]
  We propose a novel Mutual Information-driven Triple interaction Network (MITNet) for image dehazing. The first stage, named amplitude-guided haze removal, aims to recover the amplitude spectrum of the hazy images for haze removal. The second stage, named phase-guided structure refined, devotes to learning the transformation and refinement of the phase spectrum.
  arXiv  Detail & Related papers  (2023-08-14T08:23:58Z)
• NerfDiff: Single-image View Synthesis with NeRF-guided Distillation from 3D-aware Diffusion [107.67277084886929]
  Novel view synthesis from a single image requires inferring occluded regions of objects and scenes whilst simultaneously maintaining semantic and physical consistency with the input. We propose NerfDiff, which addresses this issue by distilling the knowledge of a 3D-aware conditional diffusion model (CDM) into NeRF through synthesizing and refining a set of virtual views at test time. We further propose a novel NeRF-guided distillation algorithm that simultaneously generates 3D consistent virtual views from the CDM samples, and finetunes the NeRF based on the improved virtual views.
  arXiv  Detail & Related papers  (2023-02-20T17:12:00Z)
• Zero-Shot Learning of Continuous 3D Refractive Index Maps from Discrete Intensity-Only Measurements [5.425568744312016]
  We present DeCAF as the first NF-based IDT method that can learn a high-quality continuous representation of a RI volume directly from its intensity-only and limited-angle measurements. We show on three different IDT modalities and multiple biological samples that DeCAF can generate high-contrast and artifact-free RI maps.
  arXiv  Detail & Related papers  (2021-11-27T06:05:47Z)
• Phase retrieval enhanced by quantum correlation [0.0]
  We propose a technique which exploits entanglement to enhance quantitative phase retrieval of an object in a non-interferometric setting. This protocol can find application in optical microscopy and X-ray imaging, reducing the photon dose necessary to achieve a fixed signal-to-noise ratio.
  arXiv  Detail & Related papers  (2021-09-21T10:58:48Z)
• Learning a Model-Driven Variational Network for Deformable Image Registration [89.9830129923847]
  VR-Net is a novel cascaded variational network for unsupervised deformable image registration. It outperforms state-of-the-art deep learning methods on registration accuracy. It maintains the fast inference speed of deep learning and the data-efficiency of variational model.
  arXiv  Detail & Related papers  (2021-05-25T21:37:37Z)
• Leveraging Spatial and Photometric Context for Calibrated Non-Lambertian Photometric Stereo [61.6260594326246]
  We introduce an efficient fully-convolutional architecture that can leverage both spatial and photometric context simultaneously. Using separable 4D convolutions and 2D heat-maps reduces the size and makes more efficient.
  arXiv  Detail & Related papers  (2021-03-22T18:06:58Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.