Search Results for author: Debin Zhao

Found 23 papers, 11 papers with code

Deep Unfolding Network for Image Compressed Sensing by Content-adaptive Gradient Updating and Deformation-invariant Non-local Modeling

no code implementations16 Oct 2023 Wenxue Cui, Xiaopeng Fan, Jian Zhang, Debin Zhao

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.

Image Compressed Sensing

Guided Depth Map Super-resolution: A Survey

1 code implementation19 Feb 2023 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

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.

Depth Image Upsampling Depth Map Super-Resolution +1

Task-Agnostic Learning to Accomplish New Tasks

1 code implementation9 Sep 2022 Xianqi Zhang, Xingtao Wang, Xu Liu, Wenrui Wang, Xiaopeng Fan, Debin Zhao

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.

Imitation Learning Offline RL +1

Fast Hierarchical Deep Unfolding Network for Image Compressed Sensing

no code implementations3 Aug 2022 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).

Image Compressed Sensing

Deep Attentional Guided Image Filtering

1 code implementation13 Dec 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Xiangyang Ji

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.

Collaborative Filtering Depth Image Upsampling +1

Image Compressed Sensing Using Non-local Neural Network

1 code implementation7 Dec 2021 Wenxue Cui, Shaohui Liu, Feng Jiang, Debin Zhao

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.

Image Compressed Sensing

High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion

1 code implementation4 Apr 2021 Zhiwei Zhong, Xianming Liu, Junjun Jiang, Debin Zhao, Zhiwen Chen, Xiangyang Ji

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.

Depth Map Super-Resolution

Multi-Stage Residual Hiding for Image-into-Audio Steganography

no code implementations6 Jan 2021 Wenxue Cui, Shaohui Liu, Feng Jiang, Yongliang Liu, Debin Zhao

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

no code implementations17 Aug 2018 Wenxue Cui, Tao Zhang, Shengping Zhang, Feng Jiang, WangMeng Zuo, Debin Zhao

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

1 code implementation19 Jun 2018 Wenxue Cui, Feng Jiang, Xinwei Gao, Wen Tao, Debin Zhao

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.

Image Compressed Sensing

An efficient deep convolutional laplacian pyramid architecture for CS reconstruction at low sampling ratios

1 code implementation13 Apr 2018 Wenxue Cui, Heyao Xu, Xinwei Gao, Shengping Zhang, Feng Jiang, Debin Zhao

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.

Blocking Compressive Sensing +1

An End-to-End Compression Framework Based on Convolutional Neural Networks

5 code implementations2 Aug 2017 Feng Jiang, Wen Tao, Shaohui Liu, Jie Ren, Xun Guo, Debin Zhao

The second CNN, named reconstruction convolutional neural network (RecCNN), is used to reconstruct the decoded image with high-quality in the decoding end.

Denoising Image Compression

Single Image Super-Resolution with Dilated Convolution based Multi-Scale Information Learning Inception Module

2 code implementations22 Jul 2017 Wuzhen Shi, Feng Jiang, Debin Zhao

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.

Image Restoration Image Super-Resolution

Deep Networks for Compressed Image Sensing

no code implementations22 Jul 2017 Wuzhen Shi, Feng Jiang, Shengping Zhang, Debin Zhao

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.

Image Compression

Learning Convolutional Networks for Content-weighted Image Compression

1 code implementation CVPR 2018 Mu Li, WangMeng Zuo, Shuhang Gu, Debin Zhao, David Zhang

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.

Binarization Image Compression +1

Random Walk Graph Laplacian based Smoothness Prior for Soft Decoding of JPEG Images

no code implementations7 Jul 2016 Xianming Liu, Gene Cheung, Xiaolin Wu, Debin Zhao

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.

Clustering Image Reconstruction +1

Group-based Sparse Representation for Image Restoration

1 code implementation14 May 2014 Jian Zhang, Debin Zhao, Wen Gao

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).

Compressive Sensing Deblurring +4

Image Restoration Using Joint Statistical Modeling in Space-Transform Domain

no code implementations11 May 2014 Jian Zhang, Debin Zhao, Ruiqin Xiong, Siwei Ma, Wen Gao

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.

Deblurring Image Deblurring +3

Image Compressive Sensing Recovery Using Adaptively Learned Sparsifying Basis via L0 Minimization

no code implementations30 Apr 2014 Jian Zhang, Chen Zhao, Debin Zhao, Wen Gao

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.

Blocking Compressive Sensing

Spatially Directional Predictive Coding for Block-based Compressive Sensing of Natural Images

no code implementations29 Apr 2014 Jian Zhang, Debin Zhao, Feng Jiang

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.

Compressive Sensing

Structural Group Sparse Representation for Image Compressive Sensing Recovery

no code implementations29 Apr 2014 Jian Zhang, Debin Zhao, Feng Jiang, Wen Gao

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.

Compressive Sensing

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