Compressive Sensing
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Benchmarks
Set11 cs=50%
BSD68 CS=50%
BSDS100 - 2x upscaling
Set5
Urban100 - 2x upscaling
Most implemented
Mask-guided Spectral-wise Transformer for Efficient Hyperspectral Image Reconstruction
IFR-Net: Iterative Feature Refinement Network for Compressed Sensing MRI
Learning to compress and search visual data in large-scale systems
Theoretical Linear Convergence of Unfolded ISTA and its Practical Weights and Thresholds
HDNet: High-resolution Dual-domain Learning for Spectral Compressive Imaging
Generalization Bounds for Sparse Random Feature Expansions
Papers
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 Sensing