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Found 10 papers, 3 papers with code
SC-HVPPNet: Spatial and Channel Hybrid-Attention Video Post-Processing Network with CNN and Transformer
Convolutional Neural Network (CNN) and Transformer have attracted much attention recently for video post-processing (VPP).
Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling
In this paper, we propose a new CNN based image CS coding framework using local structural sampling (dubbed CSCNet) that includes three functional modules: local structural sampling, measurement coding and Laplacian pyramid reconstruction.
Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling
In this paper, inspired by the traditional Proximal Gradient Descent (PGD) algorithm, a novel DUN for image compressed sensing (dubbed DUN-CSNet) is proposed to solve the above two issues.
Hierarchical Interactive Reconstruction Network For Video Compressive Sensing
Deep network-based image and video Compressive Sensing(CS) has attracted increasing attentions in recent years.
Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
By integrating certain optimization solvers with deep neural network, deep unfolding network (DUN) has attracted much attention in recent years for image compressed sensing (CS).
Image Compressed Sensing Using Non-local Neural Network
In this paper, a novel image CS framework using non-local neural network (NL-CSNet) is proposed, which utilizes the non-local self-similarity priors with deep network to improve the reconstruction quality.
Multi-Stage Residual Hiding for Image-into-Audio Steganography
The widespread application of audio communication technologies has speeded up audio data flowing across the Internet, which made it a popular carrier for covert communication.
Convolutional Neural Networks based Intra Prediction for HEVC
To overcome this problem, in this paper, an intra prediction convolutional neural network (IPCNN) is proposed for intra prediction, which exploits the rich context of the current block and therefore is capable of improving the accuracy of predicting the current block.
Deep neural network based sparse measurement matrix for image compressed sensing
In this paper, a Deep neural network based Sparse Measurement Matrix (DSMM) is learned by the proposed convolutional network to reduce the sampling computational complexity and improve the CS reconstruction performance.
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
To address this problem, we propose a deep convolutional Laplacian Pyramid Compressed Sensing Network (LapCSNet) for CS, which consists of a sampling sub-network and a reconstruction sub-network.
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