no code implementations • 29 Feb 2024 • Wenxue Cui, Xingtao Wang, Xiaopeng Fan, Shaohui Liu, Xinwei Gao, Debin Zhao
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.
no code implementations • CVPR 2024 • Xingtao Wang, Hongliang Wei, Xiaopeng Fan, Debin Zhao
For each noisy facet the hyper-network takes two angles as input to customize parameters for the MD network.
1 code implementation • 29 Oct 2023 • Yuanze Li, Haolin Wang, Shihao Yuan, Ming Liu, Debin Zhao, Yiwen Guo, Chen Xu, Guangming Shi, WangMeng Zuo
Existing industrial anomaly detection (IAD) methods predict anomaly scores for both anomaly detection and localization.
no code implementations • 16 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.
1 code implementation • 19 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.
1 code implementation • 9 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.
1 code implementation • 2022 IEEE International Conference on Multimedia and Expo (ICME) 2022 • Chen Hui, Feng Jiang, Shaohui Liu, Debin Zhao
Subsequently, the content-independent SPN features of different scales are fused.
no code implementations • 3 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).
1 code implementation • 13 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.
1 code implementation • 7 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.
1 code implementation • 4 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.
no code implementations • 6 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.
no code implementations • 17 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.
1 code implementation • 19 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.
1 code implementation • 13 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.
Ranked #1 on Compressive Sensing on Set5
5 code implementations • 2 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.
2 code implementations • 22 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.
Ranked #78 on Image Super-Resolution on Set14 - 4x upscaling
no code implementations • 22 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.
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.
no code implementations • 7 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.
no code implementations • CVPR 2015 • Xianming Liu, Xiaolin Wu, Jiantao Zhou, Debin Zhao
Arguably the most common cause of image degradation is compression.
1 code implementation • 14 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).
no code implementations • 11 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.
no code implementations • 30 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.
no code implementations • 29 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.
no code implementations • 29 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.