Sketched Equivariant Imaging Regularization and Deep Internal Learning for Inverse Problems

• URL: http://arxiv.org/abs/2411.05771v4
• Date: Fri, 06 Jun 2025 17:52:23 GMT
• Title: Sketched Equivariant Imaging Regularization and Deep Internal Learning for Inverse Problems
• Authors: Guixian Xu, Jinglai Li, Junqi Tang,
• Abstract summary: Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks.<n>We propose a sketched EI regularization which leverages the randomized sketching techniques for acceleration.
• Score: 4.287621751502392
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Equivariant Imaging (EI) regularization has become the de-facto technique for unsupervised training of deep imaging networks, without any need of ground-truth data. Observing that the EI-based unsupervised training paradigm currently has significant computational redundancy leading to inefficiency in high-dimensional applications, we propose a sketched EI regularization which leverages the randomized sketching techniques for acceleration. We apply our sketched EI regularization to develop an accelerated deep internal learning framework, which can be efficiently applied for test-time network adaptation. Additionally, for network adaptation tasks, we propose a parameter-efficient approach to accelerate both EI and Sketched-EI via optimizing only the normalization layers. Our numerical study on X-ray CT and multicoil magnetic resonance image reconstruction tasks demonstrate that our approach can achieve significant computational acceleration over standard EI counterpart in single-input setting and network adaptation at test time.

Related papers

• Fast Equivariant Imaging: Acceleration for Unsupervised Learning via Augmented Lagrangian and Auxiliary PnP Denoisers [4.287621751502392]
  We propose Fast Equivariant Imaging (FEI), a novel unsupervised learning framework to efficiently train deep imaging networks without ground-magnitude data.<n>FEI shows superior efficiency and performance compared to vanilla Equivariant Imaging paradigm.
  arXiv  Detail & Related papers  (2025-07-09T11:47:06Z)
• Deep regularization networks for inverse problems with noisy operators [2.665036498336221]
  A supervised learning approach is proposed for regularization of large inverse problems where the main operator is built from noisy data.<n>A neural operator maps each pattern on the right-hand side of the scattering equation to its affiliated regularization parameter.<n>We demonstrate that networks informed by the logic of discrepancy principle lead to images of higher contrast.
  arXiv  Detail & Related papers  (2025-06-08T06:19:18Z)
• Component-based Sketching for Deep ReLU Nets [55.404661149594375]
  We develop a sketching scheme based on deep net components for various tasks. We transform deep net training into a linear empirical risk minimization problem. We show that the proposed component-based sketching provides almost optimal rates in approximating saturated functions.
  arXiv  Detail & Related papers  (2024-09-21T15:30:43Z)
• 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.<n>This work considers AD in network flows using incomplete measurements.<n>We propose a novel block-successive convex approximation algorithm based on a regularized model-fitting objective.<n>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)
• Analysis of Deep Image Prior and Exploiting Self-Guidance for Image Reconstruction [13.277067849874756]
  We study how DIP recovers information from undersampled imaging measurements. We introduce a self-driven reconstruction process that concurrently optimize both the network weights and the input. Our method incorporates a novel denoiser regularization term which enables robust and stable joint estimation of both the network input and reconstructed image.
  arXiv  Detail & Related papers  (2024-02-06T15:52:23Z)
• Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution [91.3781512926942]
  Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. This work investigates the potential of network pruning for super-resolution iteration to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. We propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly network at each and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly.
  arXiv  Detail & Related papers  (2023-03-16T21:06:13Z)
• Accelerating Deep Unrolling Networks via Dimensionality Reduction [5.73658856166614]
  Deep unrolling networks are currently the state-of-the-art solutions for imaging inverse problems. For high-dimensional imaging tasks, such as X-ray CT and MRI imaging, the deep unrolling schemes typically become inefficient. We propose a new paradigm for designing efficient deep unrolling networks using dimensionality reduction schemes.
  arXiv  Detail & Related papers  (2022-08-31T11:45:21Z)
• A Fast Alternating Minimization Algorithm for Coded Aperture Snapshot Spectral Imaging Based on Sparsity and Deep Image Priors [8.890754092562918]
  Coded aperture snapshot spectral imaging (CASSI) is a technique used to reconstruct three-dimensional hyperspectral images (HSIs) This paper proposes a fast alternating minimization algorithm based on the sparsity and deep image priors (Fama-P) of natural images.
  arXiv  Detail & Related papers  (2022-06-12T03:29:14Z)
• Deep Generalized Unfolding Networks for Image Restoration [16.943609020362395]
  We propose a Deep Generalized Unfolding Network (DGUNet) for image restoration. We integrate a gradient estimation strategy into the gradient descent step of the Proximal Gradient Descent (PGD) algorithm. Our method is superior in terms of state-of-the-art performance, interpretability, and generalizability.
  arXiv  Detail & Related papers  (2022-04-28T08:39:39Z)
• Low-light Image Enhancement by Retinex Based Algorithm Unrolling and Adjustment [50.13230641857892]
  We propose a new deep learning framework for the low-light image enhancement (LIE) problem. The proposed framework contains a decomposition network inspired by algorithm unrolling, and adjustment networks considering both global brightness and local brightness sensitivity. Experiments on a series of typical LIE datasets demonstrated the effectiveness of the proposed method, both quantitatively and visually, as compared with existing methods.
  arXiv  Detail & Related papers  (2022-02-12T03:59:38Z)
• Equivariance Regularization for Image Reconstruction [5.025654873456756]
  We propose a structure-adaptive regularization scheme for solving imaging inverse problems under incomplete measurements. This regularization scheme utilizes the equivariant structure in the physics of the measurements to mitigate the ill-poseness of the inverse problem. Our proposed scheme can be applied in a plug-and-play manner alongside with any classic first-order optimization algorithm.
  arXiv  Detail & Related papers  (2022-02-10T14:38:08Z)
• Deep Image Prior using Stein's Unbiased Risk Estimator: SURE-DIP [31.408877556706376]
  Training data is scarce in many imaging applications, including ultra-high-resolution imaging. Deep image prior (DIP) algorithm was introduced for single-shot image recovery, completely eliminating the need for training data. We introduce a generalized Stein's unbiased risk estimate (GSURE) loss metric to minimize the overfitting.
  arXiv  Detail & Related papers  (2021-11-21T20:11:56Z)
• Image-specific Convolutional Kernel Modulation for Single Image Super-resolution [85.09413241502209]
  In this issue, we propose a novel image-specific convolutional modulation kernel (IKM) We exploit the global contextual information of image or feature to generate an attention weight for adaptively modulating the convolutional kernels. Experiments on single image super-resolution show that the proposed methods achieve superior performances over state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-11-16T11:05:10Z)
• Adaptive Anomaly Detection for Internet of Things in Hierarchical Edge Computing: A Contextual-Bandit Approach [81.5261621619557]
  We propose an adaptive anomaly detection scheme with hierarchical edge computing (HEC) We first construct multiple anomaly detection DNN models with increasing complexity, and associate each of them to a corresponding HEC layer. Then, we design an adaptive model selection scheme that is formulated as a contextual-bandit problem and solved by using a reinforcement learning policy network.
  arXiv  Detail & Related papers  (2021-08-09T08:45:47Z)
• Unsupervised Monocular Depth Learning with Integrated Intrinsics and Spatio-Temporal Constraints [61.46323213702369]
  This work presents an unsupervised learning framework that is able to predict at-scale depth maps and egomotion. Our results demonstrate strong performance when compared to the current state-of-the-art on multiple sequences of the KITTI driving dataset.
  arXiv  Detail & Related papers  (2020-11-02T22:26:58Z)
• Using Deep Image Priors to Generate Counterfactual Explanations [38.62513524757573]
  A deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. We propose a novel regularization strategy based on an auxiliary loss estimator jointly trained with the predictor.
  arXiv  Detail & Related papers  (2020-10-22T20:40:44Z)
• Extrapolation for Large-batch Training in Deep Learning [72.61259487233214]
  We show that a host of variations can be covered in a unified framework that we propose. We prove the convergence of this novel scheme and rigorously evaluate its empirical performance on ResNet, LSTM, and Transformer.
  arXiv  Detail & Related papers  (2020-06-10T08:22:41Z)
• Learning Deformable Image Registration from Optimization: Perspective, Modules, Bilevel Training and Beyond [62.730497582218284]
  We develop a new deep learning based framework to optimize a diffeomorphic model via multi-scale propagation. We conduct two groups of image registration experiments on 3D volume datasets including image-to-atlas registration on brain MRI data and image-to-image registration on liver CT data.
  arXiv  Detail & Related papers  (2020-04-30T03:23:45Z)
• BP-DIP: A Backprojection based Deep Image Prior [49.375539602228415]
  We propose two image restoration approaches: (i) Deep Image Prior (DIP), which trains a convolutional neural network (CNN) from scratch in test time using the degraded image; and (ii) a backprojection (BP) fidelity term, which is an alternative to the standard least squares loss that is usually used in previous DIP works. We demonstrate the performance of the proposed method, termed BP-DIP, on the deblurring task and show its advantages over the plain DIP, with both higher PSNR values and better inference run-time.
  arXiv  Detail & Related papers  (2020-03-11T17:09:12Z)

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.