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Found 24 papers, 11 papers with code
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.
Myriad: Large Multimodal Model by Applying Vision Experts for Industrial Anomaly Detection
Existing industrial anomaly detection (IAD) methods predict anomaly scores for both anomaly detection and localization.
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.
Guided Depth Map Super-resolution: A Survey
Guided depth map super-resolution (GDSR), which aims to reconstruct a high-resolution (HR) depth map from a low-resolution (LR) observation with the help of a paired HR color image, is a longstanding and fundamental problem, it has attracted considerable attention from computer vision and image processing communities.
Task-Agnostic Learning to Accomplish New Tasks
Inspired by this observation, this paper proposes a task-agnostic learning method (TAL for short) that can learn fragmented knowledge from task-agnostic data to accomplish new tasks.
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).
Deep Attentional Guided Image Filtering
Specifically, we propose an attentional kernel learning module to generate dual sets of filter kernels from the guidance and the target, respectively, and then adaptively combine them by modeling the pixel-wise dependency between the two images.
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.
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Specifically, to effectively extract and combine relevant information from LR depth and HR guidance, we propose a multi-modal attention based fusion (MMAF) strategy for hierarchical convolutional layers, including a feature enhance block to select valuable features and a feature recalibration block to unify the similarity metrics of modalities with different appearance characteristics.
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.
Ranked #1 on
Compressive Sensing
on Set5
An End-to-End Compression Framework Based on Convolutional Neural Networks
The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.
Deep Networks for Compressed Image Sensing
First of all, we train a sampling matrix via the network training instead of using a traditional manually designed one, which is much appropriate for our deep network based reconstruct process.
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
With the novel dilated convolution based inception module, the proposed end-to-end single image super-resolution network can take advantage of multi-scale information to improve image super-resolution performance.
Ranked #65 on
Image Super-Resolution
on Set14 - 4x upscaling
Learning Convolutional Networks for Content-weighted Image Compression
Therefore, the encoder, decoder, binarizer and importance map can be jointly optimized in an end-to-end manner by using a subset of the ImageNet database.
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
In this paper, we combine three image priors---Laplacian prior for DCT coefficients, sparsity prior and graph-signal smoothness prior for image patches---to construct an efficient JPEG soft decoding algorithm.
Data-Driven Sparsity-Based Restoration of JPEG-Compressed Images in Dual Transform-Pixel Domain
Arguably the most common cause of image degradation is compression.
Group-based Sparse Representation for Image Restoration
In this paper, instead of using patch as the basic unit of sparse representation, we exploit the concept of group as the basic unit of sparse representation, which is composed of nonlocal patches with similar structures, and establish a novel sparse representation modeling of natural images, called group-based sparse representation (GSR).
Image Restoration Using Joint Statistical Modeling in Space-Transform Domain
This paper presents a novel strategy for high-fidelity image restoration by characterizing both local smoothness and nonlocal self-similarity of natural images in a unified statistical manner.
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
From many fewer acquired measurements than suggested by the Nyquist sampling theory, compressive sensing (CS) theory demonstrates that, a signal can be reconstructed with high probability when it exhibits sparsity in some domain.
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Compressive Sensing (CS) theory shows that a signal can be decoded from many fewer measurements than suggested by the Nyquist sampling theory, when the signal is sparse in some domain.
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
At the encoder, for each block of compressive sensing (CS) measurements, the optimal pre-diction is selected from a set of prediction candidates that are generated by four designed directional predictive modes.
