Compressed BC-LISTA via Low-Rank Convolutional Decomposition

• URL: http://arxiv.org/abs/2601.23148v1
• Date: Fri, 30 Jan 2026 16:33:51 GMT
• Title: Compressed BC-LISTA via Low-Rank Convolutional Decomposition
• Authors: Han Wang, Yhonatan Kvich, Eduardo Pérez, Florian Römer, Yonina C. Eldar,
• Abstract summary: We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed forward and backward operators.<n>We propose a Compressed Block-Convolutional (CBC) measurement model based on a low-rank Convolutional Network (CNN) decomposition.
• Score: 47.15001096567547
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We study Sparse Signal Recovery (SSR) methods for multichannel imaging with compressed {forward and backward} operators that preserve reconstruction accuracy. We propose a Compressed Block-Convolutional (C-BC) measurement model based on a low-rank Convolutional Neural Network (CNN) decomposition that is analytically initialized from a low-rank factorization of physics-derived forward/backward operators in time delay-based measurements. We use Orthogonal Matching Pursuit (OMP) to select a compact set of basis filters from the analytic model and compute linear mixing coefficients to approximate the full model. We consider the Learned Iterative Shrinkage-Thresholding Algorithm (LISTA) network as a representative example for which the C-BC-LISTA extension is presented. In simulated multichannel ultrasound imaging across multiple Signal-to-Noise Ratios (SNRs), C-BC-LISTA requires substantially fewer parameters and smaller model size than other state-of-the-art (SOTA) methods while improving reconstruction accuracy. In ablations over OMP, Singular Value Decomposition (SVD)-based, and random initializations, OMP-initialized structured compression performs best, yielding the most efficient training and the best performance.

Related papers

• MTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images [23.22647525585002]
  We propose MTSCSNet, a CS framework based on Multiscale Summation (MTS) factorization.<n>We show that MTSCSNet achieves state-of-the-art reconstruction performance on RGB images, with notable PSNR gains and inference faster, even compared to recent diffusion-based CS methods.
  arXiv  Detail & Related papers  (2026-02-04T20:38:04Z)
• Structure-Informed Estimation for Pilot-Limited MIMO Channels via Tensor Decomposition [51.56484100374058]
  This paper formulates pilot-limited channel estimation as low-rank tensor completion from sparse observations.<n>Experiments on synthetic channels demonstrate 10-20,dB normalized mean-square error (NMSE) improvement over least-squares (LS)<n> evaluations on DeepMIMO ray-tracing channels show 24-44% additional NMSE reduction over pure tensor-based methods.
  arXiv  Detail & Related papers  (2026-02-03T23:38:05Z)
• MPQ-DMv2: Flexible Residual Mixed Precision Quantization for Low-Bit Diffusion Models with Temporal Distillation [74.34220141721231]
  We present MPQ-DMv2, an improved textbfMixed textbfPrecision textbfQuantization framework for extremely low-bit textbfDiffusion textbfModels.
  arXiv  Detail & Related papers  (2025-07-06T08:16:50Z)
• Structuring Multiple Simple Cycle Reservoirs with Particle Swarm Optimization [4.452666723220885]
  Reservoir Computing (RC) is a time-efficient computational paradigm derived from Recurrent Neural Networks (RNNs)<n>This paper introduces Multiple Simple Cycle Reservoirs (MSCRs), a multi-reservoir framework that extends Echo State Networks (ESNs)<n>We demonstrate that optimizing MSCR using Particle Swarm Optimization (PSO) outperforms existing multi-reservoir models, achieving competitive predictive performance with a lower-dimensional state space.
  arXiv  Detail & Related papers  (2025-04-06T12:25:40Z)
• DA-Flow: Dual Attention Normalizing Flow for Skeleton-based Video Anomaly Detection [52.74152717667157]
  We propose a lightweight module called Dual Attention Module (DAM) for capturing cross-dimension interaction relationships in-temporal skeletal data. It employs the frame attention mechanism to identify the most significant frames and the skeleton attention mechanism to capture broader relationships across fixed partitions with minimal parameters and flops.
  arXiv  Detail & Related papers  (2024-06-05T06:18:03Z)
• MsDC-DEQ-Net: Deep Equilibrium Model (DEQ) with Multi-scale Dilated Convolution for Image Compressive Sensing (CS) [0.0]
  Compressive sensing (CS) is a technique that enables the recovery of sparse signals using fewer measurements than traditional sampling methods. We develop an interpretable and concise neural network model for reconstructing natural images using CS. The model, called MsDC-DEQ-Net, exhibits competitive performance compared to state-of-the-art network-based methods.
  arXiv  Detail & Related papers  (2024-01-05T16:25:58Z)
• Effective Invertible Arbitrary Image Rescaling [77.46732646918936]
  Invertible Neural Networks (INN) are able to increase upscaling accuracy significantly by optimizing the downscaling and upscaling cycle jointly. A simple and effective invertible arbitrary rescaling network (IARN) is proposed to achieve arbitrary image rescaling by training only one model in this work. It is shown to achieve a state-of-the-art (SOTA) performance in bidirectional arbitrary rescaling without compromising perceptual quality in LR outputs.
  arXiv  Detail & Related papers  (2022-09-26T22:22:30Z)
• Accurate Discharge Coefficient Prediction of Streamlined Weirs by Coupling Linear Regression and Deep Convolutional Gated Recurrent Unit [2.4475596711637433]
  The present study proposes data-driven modeling techniques, as an alternative to CFD simulation, to predict the discharge coefficient based on an experimental dataset. It is found that the proposed three layer hierarchical DL algorithm consists of a convolutional layer coupled with two subsequent GRU levels, which is also hybridized with the LR method, leads to lower error metrics.
  arXiv  Detail & Related papers  (2022-04-12T01:59:36Z)
• Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression [56.83861738731913]
  Deep Neural Network (DNN) models are essential for practical applications, especially for resource limited devices. Previous unstructured or structured weight pruning methods can hardly truly accelerate inference. We propose a generalized weight unification framework at a hardware compatible micro-structured level to achieve high amount of compression and acceleration.
  arXiv  Detail & Related papers  (2021-06-15T17:22:59Z)
• Deep Low-rank plus Sparse Network for Dynamic MR Imaging [18.09395940969876]
  We propose a model-based low-rank plus sparse network, dubbed L+S-Net, for dynamic MR reconstruction. Experiments on retrospective and prospective cardiac cine datasets show that the proposed model outperforms state-of-the-art CS and existing deep learning methods.
  arXiv  Detail & Related papers  (2020-10-26T15:55:24Z)
• Kullback-Leibler Divergence-Based Fuzzy $C$-Means Clustering Incorporating Morphological Reconstruction and Wavelet Frames for Image Segmentation [152.609322951917]
  We come up with a Kullback-Leibler (KL) divergence-based Fuzzy C-Means (FCM) algorithm by incorporating a tight wavelet frame transform and a morphological reconstruction operation. The proposed algorithm works well and comes with better segmentation performance than other comparative algorithms.
  arXiv  Detail & Related papers  (2020-02-21T05:19:10Z)

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.