UPR: A Model-Driven Architecture for Deep Phase Retrieval

• URL: http://arxiv.org/abs/2003.04396v1
• Date: Mon, 9 Mar 2020 20:22:40 GMT
• Title: UPR: A Model-Driven Architecture for Deep Phase Retrieval
• Authors: Naveed Naimipour, Shahin Khobahi, Mojtaba Soltanalian
• Abstract summary: We propose a hybrid model-based data-driven deep architecture, referred to as the Unfolded Phase Retrieval (UPR) Specifically, the proposed method benefits from versatility and interpretability of well established model-based algorithms, while simultaneously benefiting from the expressive power of deep neural networks. Our numerical results illustrate the effectiveness of such hybrid deep architectures and showcase the untapped potential of data-aided methodologies to enhance the existing phase retrieval algorithms.
• Score: 14.433858410963717
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The problem of phase retrieval has been intriguing researchers for decades due to its appearance in a wide range of applications. The task of a phase retrieval algorithm is typically to recover a signal from linear phase-less measurements. In this paper, we approach the problem by proposing a hybrid model-based data-driven deep architecture, referred to as the Unfolded Phase Retrieval (UPR), that shows potential in improving the performance of the state-of-the-art phase retrieval algorithms. Specifically, the proposed method benefits from versatility and interpretability of well established model-based algorithms, while simultaneously benefiting from the expressive power of deep neural networks. Our numerical results illustrate the effectiveness of such hybrid deep architectures and showcase the untapped potential of data-aided methodologies to enhance the existing phase retrieval algorithms.

Related papers

• Adaptive Anomaly Detection in Network Flows with Low-Rank Tensor Decompositions and Deep Unrolling [9.20186865054847]
  Anomaly detection (AD) is increasingly recognized as a key component for ensuring the resilience of future communication systems. This work considers AD in network flows using incomplete measurements. We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective. Inspired by Bayesian approaches, we extend the model architecture to perform online adaptation to per-flow and per-time-step statistics.
  arXiv  Detail & Related papers  (2024-09-17T19:59:57Z)
• IRGen: Generative Modeling for Image Retrieval [82.62022344988993]
  In this paper, we present a novel methodology, reframing image retrieval as a variant of generative modeling. We develop our model, dubbed IRGen, to address the technical challenge of converting an image into a concise sequence of semantic units. Our model achieves state-of-the-art performance on three widely-used image retrieval benchmarks and two million-scale datasets.
  arXiv  Detail & Related papers  (2023-03-17T17:07:36Z)
• Deep Unfolding of the DBFB Algorithm with Application to ROI CT Imaging with Limited Angular Density [15.143939192429018]
  This paper presents a new method for reconstructing regions of interest (ROI) from a limited number of computed (CT) measurements. Deep methods are fast, and they can reach high reconstruction quality by leveraging information from datasets. We introduce an unfolding neural network called UDBFB designed for ROI reconstruction from limited data.
  arXiv  Detail & Related papers  (2022-09-27T09:10:57Z)
• PR-DAD: Phase Retrieval Using Deep Auto-Decoders [0.0]
  PR-DAD (Phase Retrieval Using Deep Auto- Decoders) is a novel architecture based on mathematical modeling of the phase retrieval problem. The architecture provides experimental results that surpass all current results.
  arXiv  Detail & Related papers  (2022-04-18T21:20:01Z)
• Deep Equilibrium Assisted Block Sparse Coding of Inter-dependent Signals: Application to Hyperspectral Imaging [71.57324258813675]
  A dataset of inter-dependent signals is defined as a matrix whose columns demonstrate strong dependencies. A neural network is employed to act as structure prior and reveal the underlying signal interdependencies. Deep unrolling and Deep equilibrium based algorithms are developed, forming highly interpretable and concise deep-learning-based architectures.
  arXiv  Detail & Related papers  (2022-03-29T21:00:39Z)
• Deep Unrolled Recovery in Sparse Biological Imaging [62.997667081978825]
  Deep algorithm unrolling is a model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance gains of supervised deep learning. This framework is well-suited to applications in biological imaging, where physics-based models exist to describe the measurement process and the information to be recovered is often highly structured.
  arXiv  Detail & Related papers  (2021-09-28T20:22:44Z)
• Phase Retrieval using Expectation Consistent Signal Recovery Algorithm based on Hypernetwork [73.94896986868146]
  Phase retrieval is an important component in modern computational imaging systems. Recent advances in deep learning have opened up a new possibility for robust and fast PR. We develop a novel framework for deep unfolding to overcome the existing limitations.
  arXiv  Detail & Related papers  (2021-01-12T08:36:23Z)
• Unfolded Algorithms for Deep Phase Retrieval [16.14838937433809]
  We propose a hybrid model-based data-driven deep architecture, referred to as Unfolded Phase Retrieval (UPR) The proposed method benefits from versatility and interpretability of well-established model-based algorithms. We consider a joint design of the sensing matrix and the signal processing algorithm and utilize the deep unfolding technique in the process.
  arXiv  Detail & Related papers  (2020-12-21T03:46:17Z)
• Solving Sparse Linear Inverse Problems in Communication Systems: A Deep Learning Approach With Adaptive Depth [51.40441097625201]
  We propose an end-to-end trainable deep learning architecture for sparse signal recovery problems. The proposed method learns how many layers to execute to emit an output, and the network depth is dynamically adjusted for each task in the inference phase.
  arXiv  Detail & Related papers  (2020-10-29T06:32:53Z)
• Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search [50.40004966087121]
  We introduce a new reinforcement learning based neural architecture search (NAS) methodology for generative adversarial network (GAN) architecture search. The key idea is to formulate the GAN architecture search problem as a Markov decision process (MDP) for smoother architecture sampling. We exploit an off-policy GAN architecture search algorithm that makes efficient use of the samples generated by previous policies.
  arXiv  Detail & Related papers  (2020-07-17T18:29:17Z)

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.