
Spatial–spectral FFPNet: AttentionBased Pyramid Network for Segmentation and Classification of Remote Sensing Images
We consider the problem of segmentation and classification of highresol...
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Growing Efficient Deep Networks by Structured Continuous Sparsification
We develop an approach to training deep networks while dynamically adjus...
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Various Total Variation for Snapshot Video Compressive Imaging
Sampling highdimensional images is challenging due to limited availabil...
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The Power of Triply Complementary Priors for Image Compressive Sensing
Recent works that utilized deep models have achieved superior results in...
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PlugandPlay Algorithms for Largescale Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) aims to capture the highdimensional ...
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The Networkbased Candidate Forwarding Set Optimization Approach for Opportunistic Routing in Wireless Sensor Network
In wireless sensor networks (WSNs), the opportunistic routing has better...
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Geographical and Topology Control based Opportunistic Routing for Ad Hoc Networks
The opportunistic routing has great advantages on improving packet deliv...
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Trapezoidal Sketch: A Sketch Structure for Frequency Estimation of Data Streams
The sketch is one of the typical and widely used data structures for est...
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Pairwisebased MultiAttribute Decision Making Approach for Wireless Network
In the wireless network applications, such as routing decision, network ...
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Game Theory based Joint Task Offloading and Resources Allocation Algorithm for Mobile Edge Computing
Mobile edge computing (MEC) has emerged for reducing energy consumption ...
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Dense Color Constancy with Effective Edge Augmentation
Recently, computational color constancy via convolutional neural network...
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LISR: Image Superresolution under Hardware Constraints
We investigate the image superresolution problem by considering the pow...
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Using autoencoders for solving illposed linear inverse problems
Compressed sensing algorithms recover a signal from its underdetermined...
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Snapshot compressed sensing: performance bounds and algorithms
Snapshot compressed sensing (CS) refers to compressive imaging systems w...
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Rank Minimization for Snapshot Compressive Imaging
Snapshot compressive imaging (SCI) refers to compressive imaging systems...
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From Rank Estimation to Rank Approximation: Rank Residual Constraint for Image Denoising
Inspired by the recent advances of Generative Adversarial Networks (GAN)...
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Group Sparsity Residual with NonLocal Samples for Image Denoising
Inspired by groupbased sparse coding, recently proposed group sparsity ...
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Nonlocal LowRank Tensor Factor Analysis for Image Restoration
Lowrank signal modeling has been widely leveraged to capture nonlocal ...
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Bridge the Gap Between Group Sparse Coding and Rank Minimization via Adaptive Dictionary Learning
Both sparse coding and rank minimization have led to great successes in ...
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Image Compression Based on Compressive Sensing: EndtoEnd Comparison with JPEG
We present an endtoend image compression system based on compressive s...
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Blockwise Lensless Compressive Camera
The existing lensless compressive camera (L^2C^2) Huang13ICIP suffers fr...
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Compressive Sensing via Convolutional Factor Analysis
We solve the compressive sensing problem via convolutional factor analys...
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Variational Autoencoder for Deep Learning of Images, Labels and Captions
A novel variational autoencoder is developed to model images, as well as...
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A Deep Generative Deconvolutional Image Model
A deep generative model is developed for representation and analysis of ...
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Compressive Sensing via LowRank Gaussian Mixture Models
We develop a new compressive sensing (CS) inversion algorithm by utilizi...
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NonGaussian Discriminative Factor Models via the MaxMargin RankLikelihood
We consider the problem of discriminative factor analysis for data that ...
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A Generative Model for Deep Convolutional Learning
A generative model is developed for deep (multilayered) convolutional d...
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Compressive Hyperspectral Imaging with Side Information
A blind compressive sensing algorithm is proposed to reconstruct hypersp...
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Generative Deep Deconvolutional Learning
A generative Bayesian model is developed for deep (multilayer) convolut...
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Classification and Reconstruction of HighDimensional Signals from LowDimensional Features in the Presence of Side Information
This paper offers a characterization of fundamental limits on the classi...
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TreeStructure Bayesian Compressive Sensing for Video
A Bayesian compressive sensing framework is developed for video reconstr...
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Multiscale Shrinkage and Lévy Processes
A new shrinkagebased construction is developed for a compressible vecto...
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