Papers Compressive Sensing
“Compressive Sensing” 태그가 달린 논문 624편 · 필터 해제
Learning the Mathematical Property for Designing Low Mutual Coherence Binary Sensing Matrices
In this research work, we are constructing the sensing matrix, which is essential for the success of the compressive sensing technique. We have chosen a learning-based technique for the construction of the sensing matrix…
Compressive SensingPlug-and-Play Volumetric Reconstruction for Compressive Sensing Light-Sheet Microscopy
We investigate volumetric reconstruction for compressive sensing light-sheet microscopy (CS-LSM), where fast volumetric imaging is achieved by encoding multiple axial planes into each camera exposure. To recover the unde…
Compressive SensingSTRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Training Data Attribution (TDA) seeks to trace a model's predictions back to its training data. The gold standard for TDA relies on causal interventions, observing how a model changes when data is added or removed, but r…
Compressive SensingBeyond Single Solution: Multi-Hypothesis Collaborative Deep Unfolding Network for Image Compressive Sensing
Recent deep unfolding networks (DUNs) have advanced Compressive Sensing (CS) by effectively integrating iterative optimization with deep learning architectures. However, most CS approaches predominantly confine their inf…
Compressive SensingDeep Probabilistic Unfolding for Quantized Compressive Sensing
We propose a deep probabilistic unfolding model to address the classical quantized compressive sensing problem that leverages an unfolding framework to enhance the reconstruction accuracy and efficiency. Unlike previous …
Compressive SensingDual-Path Hyperprior Informed Deep Unfolding Network for Image Compressive Sensing
Recent Deep Unfolding Networks (DUNs) have significantly advanced Compressive Sensing (CS) by integrating iterative optimization with deep networks. However, existing DUNs still suffer from two challenges: 1) Reliance on…
Compressive SensingInformation Density as a Quantitative Measure for AI-enabled Virtual Sensing: Feasibility and Limits
Modern IoT and sensor networks generate vast amounts of data, posing significant challenges for storage, transmission, and real-time processing. Traditional approaches, such as compressive sensing and machine learning-ba…
Compressive SensingCompressive sensing inspired self-supervised single-pixel imaging
Single-pixel imaging (SPI) is a promising imaging modality with distinctive advantages in strongly perturbed environments. Existing SPI methods lack physical sparsity constraints and overlook the integration of local and…
Compressive SensingDeep LoRA-Unfolding Networks for Image Restoration
Deep unfolding networks (DUNs), combining conventional iterative optimization algorithms and deep neural networks into a multi-stage framework, have achieved remarkable accomplishments in Image Restoration (IR), such as …
Compressive SensingImage RestorationMTS-CSNet: Multiscale Tensor Factorization for Deep Compressive Sensing on RGB Images
Deep learning based compressive sensing (CS) methods typically learn sampling operators using convolutional or block wise fully connected layers, which limit receptive fields and scale poorly for high dimensional data. W…
Dimensionality ReductionCompressive SensingDeMark: A Query-Free Black-Box Attack on Deepfake Watermarking Defenses
The rapid proliferation of realistic deepfakes has raised urgent concerns over their misuse, motivating the use of defensive watermarks in synthetic images for reliable detection and provenance tracking. However, this de…
Compressive SensingEfficient Hyperspectral Image Reconstruction Using Lightweight Separate Spectral Transformers
Hyperspectral imaging (HSI) is essential across various disciplines for its capacity to capture rich spectral information. However, efficiently reconstructing hyperspectral images from compressive sensing measurements pr…
Image ReconstructionCompressive SensingPrimal: A Unified Deterministic Framework for Quasi-Orthogonal Hashing and Manifold Learning
We present Primal, a deterministic feature mapping framework that harnesses the number-theoretic independence of prime square roots to construct robust, tunable vector representations. Diverging from standard stochastic …
Compressive SensingA Plug-and-Play Framework for Volumetric Light-Sheet Image Reconstruction
Cardiac contraction is a rapid, coordinated process that unfolds across three-dimensional tissue on millisecond timescales. Traditional optical imaging is often inadequate for capturing dynamic cellular structure in the …
Image ReconstructionCompressive SensingDiffPace: Diffusion-based Plug-and-play Augmented Channel Estimation in mmWave and Terahertz Ultra-Massive MIMO Systems
Millimeter-wave (mmWave) and Terahertz (THz)-band communications hold great promise in meeting the growing data-rate demands of next-generation wireless networks, offering abundant bandwidth. To mitigate the severe path …
Computational EfficiencyCompressive SensingA Statistical Method for Attack-Agnostic Adversarial Attack Detection with Compressive Sensing Comparison
Adversarial attacks present a significant threat to modern machine learning systems. Yet, existing detection methods often lack the ability to detect unseen attacks or detect different attack types with a high level of a…
Compressive SensingAdversarial AttackNPN: Non-Linear Projections of the Null-Space for Imaging Inverse Problems
Imaging inverse problems aim to recover high-dimensional signals from undersampled, noisy measurements, a fundamentally ill-posed task with infinite solutions in the null-space of the sensing operator. To resolve this am…
Compressive SensingFlow-Matching Guided Deep Unfolding for Hyperspectral Image Reconstruction
Hyperspectral imaging (HSI) provides rich spatial-spectral information but remains costly to acquire due to hardware limitations and the difficulty of reconstructing three-dimensional data from compressed measurements. A…
Image ReconstructionCompressive SensingEquivariant Splitting: Self-supervised learning from incomplete data
Self-supervised learning for inverse problems allows to train a reconstruction network from noise and/or incomplete data alone. These methods have the potential of enabling learning-based solutions when obtaining ground-…
Self-Supervised LearningCompressive SensingImage InpaintingProbabilistic and nonlinear compressive sensing
We present a smooth probabilistic reformulation of $\ell_0$ regularized regression that does not require Monte Carlo sampling and allows for the computation of exact gradients, facilitating rapid convergence to local opt…
Compressive Sensing