189 citations · 928 across the 27 of their papers we have counts for
19 papers · 1 filter
Guided Depth Map Super-resolution: A Survey
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
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…
Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing
Wenxue Cui, Shaohui Liu, Debin Zhao
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). H…
Deep Attentional Guided Image Filtering
Zhiwei Zhong, Xianming Liu, Junjun Jiang +2
Guided filter is a fundamental tool in computer vision and computer graphics which aims to transfer structure information from guidance image to target image. Most existing methods…
High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion
Zhiwei Zhong, Xianming Liu, Junjun Jiang +3
Depth map records distance between the viewpoint and objects in the scene, which plays a critical role in many real-world applications. However, depth map captured by consumer-grad…
Multi-Stage Residual Hiding for Image-into-Audio Steganography
Wenxue Cui, Shaohui Liu, Feng Jiang +2
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. In t…
Convolutional Neural Networks based Intra Prediction for HEVC
Wenxue Cui, Tao Zhang, Shengping Zhang +3
Traditional intra prediction methods for HEVC rely on using the nearest reference lines for predicting a block, which ignore much richer context between the current block and its n…
Deep neural network based sparse measurement matrix for image compressed sensing
Wenxue Cui, Feng Jiang, Xinwei Gao +2
Gaussian random matrix (GRM) has been widely used to generate linear measurements in compressed sensing (CS) of natural images. However, there actually exist two disadvantages with…
An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios
Wenxue Cui, Heyao Xu, Xinwei Gao +3
The compressed sensing (CS) has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconstruction…
An End-to-End Compression Framework Based on Convolutional Neural Networks
Feng Jiang, Wen Tao, Shaohui Liu +3
Deep learning, e.g., convolutional neural networks (CNNs), has achieved great success in image processing and computer vision especially in high level vision applications such as r…
Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module
Wuzhen Shi, Feng Jiang, Debin Zhao
Traditional works have shown that patches in a natural image tend to redundantly recur many times inside the image, both within the same scale, as well as across different scales.…
Deep Networks for Compressed Image Sensing
Wuzhen Shi, Feng Jiang, Shengping Zhang +1
The compressed sensing (CS) theory has been successfully applied to image compression in the past few years as most image signals are sparse in a certain domain. Several CS reconst…
Learning Convolutional Networks for Content-weighted Image Compression
Mu Li, Wangmeng Zuo, Shuhang Gu +2
Lossy image compression is generally formulated as a joint rate-distortion optimization to learn encoder, quantizer, and decoder. However, the quantizer is non-differentiable, and…
Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images
Xianming Liu, Gene Cheung, Xiaolin Wu +1
Given the prevalence of JPEG compressed images, optimizing image reconstruction from the compressed format remains an important problem. Instead of simply reconstructing a pixel bl…
Group-based Sparse Representation for Image Restoration
Jian Zhang, Debin Zhao, Wen Gao
Traditional patch-based sparse representation modeling of natural images usually suffer from two problems. First, it has to solve a large-scale optimization problem with high compu…
Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization
Jian Zhang, Chen Zhao, Debin Zhao +1
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 prob…
Structural Group Sparse Representation for Image Compressive Sensing Recovery
Jian Zhang, Debin Zhao, Feng Jiang +1
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 dom…
Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images
Jian Zhang, Debin Zhao, Feng Jiang
A novel coding strategy for block-based compressive sens-ing named spatially directional predictive coding (SDPC) is proposed, which efficiently utilizes the intrinsic spatial cor-…
Image Super-Resolution via Dual-Dictionary Learning And Sparse Representation
Jian Zhang, Chen Zhao, Ruiqin Xiong +2
Learning-based image super-resolution aims to reconstruct high-frequency (HF) details from the prior model trained by a set of high- and low-resolution image patches. In this paper…
Improved Total Variation based Image Compressive Sensing Recovery by Nonlocal Regularization
Jian Zhang, Shaohui Liu, Debin Zhao +2
Recently, total variation (TV) based minimization algorithms have achieved great success in compressive sensing (CS) recovery for natural images due to its virtue of preserving edg…