Search Results for author: Weisheng Dong

Found 13 papers, 3 papers with code

Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

no code implementations30 Aug 2021 Yang Wu, Dingheng Wang, Xiaotong Lu, Fan Yang, Guoqi Li, Weisheng Dong, Jianbo Shi

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence.

Lightweight Image Super-Resolution with Hierarchical and Differentiable Neural Architecture Search

no code implementations9 May 2021 Han Huang, Li Shen, Chaoyang He, Weisheng Dong, HaoZhi Huang, Guangming Shi

Specifically, the cell-level search space is designed based on an information distillation mechanism, focusing on the combinations of lightweight operations and aiming to build a more lightweight and accurate SR structure.

Image Super-Resolution Neural Architecture Search +1

Generalizable No-Reference Image Quality Assessment via Deep Meta-learning

1 code implementation IEEE Transactions on Circuits and Systems for Video Technology 2021 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, and Guangming Shi

Based on these two task sets, an optimization-based meta-learning is proposed to learn the generalized NR-IQA model, which can be directly used to evaluate the quality of images with unseen distortions.

Meta-Learning No-Reference Image Quality Assessment

Searching Efficient Model-guided Deep Network for Image Denoising

no code implementations6 Apr 2021 Qian Ning, Weisheng Dong, Xin Li, Jinjian Wu, Leida Li, Guangming Shi

Similar to the success of NAS in high-level vision tasks, it is possible to find a memory and computationally efficient solution via NAS with highly competent denoising performance.

Image Denoising Neural Architecture Search

Deep Gaussian Scale Mixture Prior for Spectral Compressive Imaging

1 code implementation CVPR 2021 Tao Huang, Weisheng Dong, Xin Yuan, Jinjian Wu, Guangming Shi

Different from existing GSM models using hand-crafted scale priors (e. g., the Jeffrey's prior), we propose to learn the scale prior through a deep convolutional neural network (DCNN).

MetaIQA: Deep Meta-learning for No-Reference Image Quality Assessment

1 code implementation CVPR 2020 Hancheng Zhu, Leida Li, Jinjian Wu, Weisheng Dong, Guangming Shi

The underlying idea is to learn the meta-knowledge shared by human when evaluating the quality of images with various distortions, which can then be adapted to unknown distortions easily.

Meta-Learning No-Reference Image Quality Assessment

Learning Hybrid Sparsity Prior for Image Restoration: Where Deep Learning Meets Sparse Coding

no code implementations18 Jul 2018 Fangfang Wu, Weisheng Dong, Guangming Shi, Xin Li

State-of-the-art approaches toward image restoration can be classified into model-based and learning-based.

Image Restoration

ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning

no code implementations31 Jan 2018 Xiaotong Lu, Weisheng Dong, Peiyao Wang, Guangming Shi, Xuemei Xie

Instead of reconstructing individual blocks, the whole image is reconstructed from the linear convolutional measurements.

Compressive Sensing

Denoising Prior Driven Deep Neural Network for Image Restoration

no code implementations21 Jan 2018 Weisheng Dong, Peiyao Wang, Wotao Yin, Guangming Shi, Fangfang Wu, Xiaotong Lu

Then, the iterative process is unfolded into a deep neural network, which is composed of multiple denoisers modules interleaved with back-projection (BP) modules that ensure the observation consistencies.

Deblurring Image Denoising +2

Learning Parametric Sparse Models for Image Super-Resolution

no code implementations NeurIPS 2016 Yongbo Li, Weisheng Dong, Xuemei Xie, Guangming Shi, Xin Li, Donglai Xu

More specifically, the parametric sparse prior of the desirable high-resolution (HR) image patches are learned from both the input low-resolution (LR) image and a training image dataset.

Image Super-Resolution

Learning Parametric Distributions for Image Super-Resolution: Where Patch Matching Meets Sparse Coding

no code implementations ICCV 2015 Yongbo Li, Weisheng Dong, Guangming Shi, Xuemei Xie

Existing approaches toward Image super-resolution (SR) is often either data-driven (e. g., based on internet-scale matching and web image retrieval) or model-based (e. g., formulated as an Maximizing a Posterior estimation problem).

Image Retrieval Image Super-Resolution +1

Low-Rank Tensor Approximation With Laplacian Scale Mixture Modeling for Multiframe Image Denoising

no code implementations ICCV 2015 Weisheng Dong, Guangyu Li, Guangming Shi, Xin Li, Yi Ma

Patch-based low-rank models have shown effective in exploiting spatial redundancy of natural images especially for the application of image denoising.

Dictionary Learning Image Denoising

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