Deep Iterative Phase Retrieval for Ptychography

• URL: http://arxiv.org/abs/2202.10573v1
• Date: Thu, 17 Feb 2022 09:13:35 GMT
• Title: Deep Iterative Phase Retrieval for Ptychography
• Authors: Simon Welker, Tal Peer, Henry N. Chapman, Timo Gerkmann
• Abstract summary: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. In this work we consider ptychography, a sub-field of diffractive imaging, where objects are reconstructed from multiple overlapping diffraction images. We propose an augmentation of existing iterative phase retrieval algorithms with a neural network designed for refining the result of each iteration.
• Score: 13.49645012479288
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: One of the most prominent challenges in the field of diffractive imaging is the phase retrieval (PR) problem: In order to reconstruct an object from its diffraction pattern, the inverse Fourier transform must be computed. This is only possible given the full complex-valued diffraction data, i.e. magnitude and phase. However, in diffractive imaging, generally only magnitudes can be directly measured while the phase needs to be estimated. In this work we specifically consider ptychography, a sub-field of diffractive imaging, where objects are reconstructed from multiple overlapping diffraction images. We propose an augmentation of existing iterative phase retrieval algorithms with a neural network designed for refining the result of each iteration. For this purpose we adapt and extend a recently proposed architecture from the speech processing field. Evaluation results show the proposed approach delivers improved convergence rates in terms of both iteration count and algorithm runtime.

Related papers

• PtychoFormer: A Transformer-based Model for Ptychographic Phase Retrieval [9.425754476649796]
  We present a hierarchical transformer-based model for data-driven single-shot ptychographic phase retrieval. Our model exhibits tolerance to sparsely scanned diffraction patterns and achieves up to 3600 times faster imaging speed than the extended ptychographic iterative engine (ePIE)
  arXiv  Detail & Related papers  (2024-10-22T19:26:05Z)
• Deep Equilibrium Diffusion Restoration with Parallel Sampling [120.15039525209106]
  Diffusion model-based image restoration (IR) aims to use diffusion models to recover high-quality (HQ) images from degraded images, achieving promising performance. Most existing methods need long serial sampling chains to restore HQ images step-by-step, resulting in expensive sampling time and high computation costs. In this work, we aim to rethink the diffusion model-based IR models through a different perspective, i.e., a deep equilibrium (DEQ) fixed point system, called DeqIR.
  arXiv  Detail & Related papers  (2023-11-20T08:27:56Z)
• Single-shot Phase Retrieval from a Fractional Fourier Transform Perspective [12.490990352972695]
  We present a novel single-shot phase retrieval paradigm from a fractional Fourier transform perspective. The intensity measurement in the FrFT domain proves highly effective in alleviating the ambiguities of phase retrieval. The proposed self-supervised reconstruction approach harnesses the fast discrete algorithm of FrFT alongside untrained neural network priors.
  arXiv  Detail & Related papers  (2023-11-18T03:11:31Z)
• Exploring Invariance in Images through One-way Wave Equations [96.90549064390608]
  In this paper, we empirically reveal an invariance over images-images share a set of one-way wave equations with latent speeds. We demonstrate it using an intuitive encoder-decoder framework where each image is encoded into its corresponding initial condition.
  arXiv  Detail & Related papers  (2023-10-19T17:59:37Z)
• PtychoDV: Vision Transformer-Based Deep Unrolling Network for Ptychographic Image Reconstruction [12.780951605821238]
  PtychoDV is a novel deep model-based network designed for efficient, high-quality ptychographic image reconstruction. Results on simulated data demonstrate that PtychoDV is capable of outperforming existing deep learning methods for this problem.
  arXiv  Detail & Related papers  (2023-10-11T14:01:36Z)
• Deep Richardson-Lucy Deconvolution for Low-Light Image Deblurring [48.80983873199214]
  We develop a data-driven approach to model the saturated pixels by a learned latent map. Based on the new model, the non-blind deblurring task can be formulated into a maximum a posterior (MAP) problem. To estimate high-quality deblurred images without amplified artifacts, we develop a prior estimation network.
  arXiv  Detail & Related papers  (2023-08-10T12:53:30Z)
• DeepRM: Deep Recurrent Matching for 6D Pose Refinement [77.34726150561087]
  DeepRM is a novel recurrent network architecture for 6D pose refinement. The architecture incorporates LSTM units to propagate information through each refinement step. DeepRM achieves state-of-the-art performance on two widely accepted challenging datasets.
  arXiv  Detail & Related papers  (2022-05-28T16:18:08Z)
• Classification and reconstruction of spatially overlapping phase images using diffractive optical networks [0.0]
  Diffractive optical networks unify wave optics and deep learning to all-optically compute a given machine learning or computational imaging task as the light propagates from the input to the output plane. We show that through a task-specific training process, diffractive networks can all-optically and simultaneously classify two different randomly-selected, spatially overlapping phase images at the input. In addition to all-optical classification of overlapping phase objects, we also demonstrate the reconstruction of these phase images based on a shallow electronic neural network.
  arXiv  Detail & Related papers  (2021-08-18T05:15:05Z)
• TFill: Image Completion via a Transformer-Based Architecture [69.62228639870114]
  We propose treating image completion as a directionless sequence-to-sequence prediction task. We employ a restrictive CNN with small and non-overlapping RF for token representation. In a second phase, to improve appearance consistency between visible and generated regions, a novel attention-aware layer (AAL) is introduced.
  arXiv  Detail & Related papers  (2021-04-02T01:42:01Z)
• End-to-end Interpretable Learning of Non-blind Image Deblurring [102.75982704671029]
  Non-blind image deblurring is typically formulated as a linear least-squares problem regularized by natural priors on the corresponding sharp picture's gradients. We propose to precondition the Richardson solver using approximate inverse filters of the (known) blur and natural image prior kernels.
  arXiv  Detail & Related papers  (2020-07-03T15:45:01Z)
• u-net CNN based fourier ptychography [5.46367622374939]
  We propose a new retrieval algorithm that is based on convolutional neural networks. Experiments demonstrate that our model achieves better reconstruction results and is more robust under system aberrations.
  arXiv  Detail & Related papers  (2020-03-16T22:48:44Z)

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.