Non-Iterative Phase Retrieval With Cascaded Neural Networks

• URL: http://arxiv.org/abs/2106.10195v1
• Date: Fri, 18 Jun 2021 15:52:12 GMT
• Title: Non-Iterative Phase Retrieval With Cascaded Neural Networks
• Authors: Tobias Uelwer and Tobias Hoffmann and Stefan Harmeling
• Abstract summary: We present a deep neural network cascade that reconstructs an image successively on different resolutions from its non-oversampled Fourier magnitude. We evaluate our method on four different datasets.
• Score: 0.966840768820136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fourier phase retrieval is the problem of reconstructing a signal given only the magnitude of its Fourier transformation. Optimization-based approaches, like the well-established Gerchberg-Saxton or the hybrid input output algorithm, struggle at reconstructing images from magnitudes that are not oversampled. This motivates the application of learned methods, which allow reconstruction from non-oversampled magnitude measurements after a learning phase. In this paper, we want to push the limits of these learned methods by means of a deep neural network cascade that reconstructs the image successively on different resolutions from its non-oversampled Fourier magnitude. We evaluate our method on four different datasets (MNIST, EMNIST, Fashion-MNIST, and KMNIST) and demonstrate that it yields improved performance over other non-iterative methods and optimization-based methods.

Related papers

• Verification of Geometric Robustness of Neural Networks via Piecewise Linear Approximation and Lipschitz Optimisation [57.10353686244835]
  We address the problem of verifying neural networks against geometric transformations of the input image, including rotation, scaling, shearing, and translation. The proposed method computes provably sound piecewise linear constraints for the pixel values by using sampling and linear approximations in combination with branch-and-bound Lipschitz. We show that our proposed implementation resolves up to 32% more verification cases than present approaches.
  arXiv  Detail & Related papers  (2024-08-23T15:02:09Z)
• Fine Structure-Aware Sampling: A New Sampling Training Scheme for Pixel-Aligned Implicit Models in Single-View Human Reconstruction [105.46091601932524]
  We introduce Fine Structured-Aware Sampling (FSS) to train pixel-aligned implicit models for single-view human reconstruction. FSS proactively adapts to the thickness and complexity of surfaces. It also proposes a mesh thickness loss signal for pixel-aligned implicit models.
  arXiv  Detail & Related papers  (2024-02-29T14:26:46Z)
• Reinforcement Learning for Sampling on Temporal Medical Imaging Sequences [0.0]
  In this work, we apply double deep Q-learning and REINFORCE algorithms to learn the sampling strategy for dynamic image reconstruction. We consider the data in the format of time series, and the reconstruction method is a pre-trained autoencoder-typed neural network. We present a proof of concept that reinforcement learning algorithms are effective to discover the optimal sampling pattern.
  arXiv  Detail & Related papers  (2023-08-28T23:55:23Z)
• A training-free recursive multiresolution framework for diffeomorphic deformable image registration [6.929709872589039]
  We propose a novel diffeomorphic training-free approach for deformable image registration. The proposed architecture is simple in design. The moving image is warped successively at each resolution and finally aligned to the fixed image. The entire system is end-to-end and optimized for each pair of images from scratch.
  arXiv  Detail & Related papers  (2022-02-01T15:17:17Z)
• Deblurring via Stochastic Refinement [85.42730934561101]
  We present an alternative framework for blind deblurring based on conditional diffusion models. Our method is competitive in terms of distortion metrics such as PSNR.
  arXiv  Detail & Related papers  (2021-12-05T04:36:09Z)
• Learning Discriminative Shrinkage Deep Networks for Image Deconvolution [122.79108159874426]
  We propose an effective non-blind deconvolution approach by learning discriminative shrinkage functions to implicitly model these terms. Experimental results show that the proposed method performs favorably against the state-of-the-art ones in terms of efficiency and accuracy.
  arXiv  Detail & Related papers  (2021-11-27T12:12:57Z)
• Conditional Variational Autoencoder for Learned Image Reconstruction [5.487951901731039]
  We develop a novel framework that approximates the posterior distribution of the unknown image at each query observation. It handles implicit noise models and priors, it incorporates the data formation process (i.e., the forward operator), and the learned reconstructive properties are transferable between different datasets.
  arXiv  Detail & Related papers  (2021-10-22T10:02:48Z)
• Deep Unfolded Recovery of Sub-Nyquist Sampled Ultrasound Image [94.42139459221784]
  We propose a reconstruction method from sub-Nyquist samples in the time and spatial domain, that is based on unfolding the ISTA algorithm. Our method allows reducing the number of array elements, sampling rate, and computational time while ensuring high quality imaging performance.
  arXiv  Detail & Related papers  (2021-03-01T19:19:38Z)
• Learning Frequency Domain Approximation for Binary Neural Networks [68.79904499480025]
  We propose to estimate the gradient of sign function in the Fourier frequency domain using the combination of sine functions for training BNNs. The experiments on several benchmark datasets and neural architectures illustrate that the binary network learned using our method achieves the state-of-the-art accuracy.
  arXiv  Detail & Related papers  (2021-03-01T08:25:26Z)
• Sampling possible reconstructions of undersampled acquisitions in MR imaging [9.75702493778194]
  Undersampling the k-space during MR saves time, however results in an ill-posed inversion problem, leading to an infinite set of images as possible solutions. Traditionally, this is tackled as a reconstruction problem by searching for a single "best" image out of this solution set according to some chosen regularization or prior. We propose a method that instead returns multiple images which are possible under the acquisition model and the chosen prior to capture the uncertainty in the inversion process.
  arXiv  Detail & Related papers  (2020-09-30T18:20:06Z)
• Solving Phase Retrieval with a Learned Reference [18.76940558836028]
  Fourier phase retrieval is a classical problem that deals with the recovery of an image from the amplitude measurements of its Fourier coefficients. In this paper, we assume that a known (learned) reference is added to the signal before capturing the amplitude measurements. Our method is inspired by the principle of adding a reference signal in holography.
  arXiv  Detail & Related papers  (2020-07-29T06:17:25Z)

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.