Search Results for author: Shuhang Gu

Found 41 papers, 17 papers with code

MFAGAN: A Compression Framework for Memory-Efficient On-Device Super-Resolution GAN

no code implementations27 Jul 2021 Wenlong Cheng, Mingbo Zhao, Zhiling Ye, Shuhang Gu

In this paper, we propose a novel compression framework \textbf{M}ulti-scale \textbf{F}eature \textbf{A}ggregation Net based \textbf{GAN} (MFAGAN) for reducing the memory access cost of the generator.

Image Super-Resolution Neural Architecture Search

Improving Facial Attribute Recognition by Group and Graph Learning

no code implementations28 May 2021 Zhenghao Chen, Shuhang Gu, Feng Zhu, Jing Xu, Rui Zhao

For the spatial correlation, we aggregate attributes with spatial similarity into a part-based group and then introduce a Group Attention Learning to generate the group attention and the part-based group feature.

Graph Learning

Improving Deep Video Compression by Resolution-adaptive Flow Coding

no code implementations ECCV 2020 Zhihao Hu, Zhenghao Chen, Dong Xu, Guo Lu, Wanli Ouyang, Shuhang Gu

In this work, we propose a new framework called Resolution-adaptive Flow Coding (RaFC) to effectively compress the flow maps globally and locally, in which we use multi-resolution representations instead of single-resolution representations for both the input flow maps and the output motion features of the MV encoder.

Optical Flow Estimation Video Compression

You Only Look Yourself: Unsupervised and Untrained Single Image Dehazing Neural Network

no code implementations30 Jun 2020 Boyun Li, Yuanbiao Gou, Shuhang Gu, Jerry Zitao Liu, Joey Tianyi Zhou, Xi Peng

In this paper, we study two challenging and less-touched problems in single image dehazing, namely, how to make deep learning achieve image dehazing without training on the ground-truth clean image (unsupervised) and a image collection (untrained).

Image Dehazing Single Image Dehazing

The Heterogeneity Hypothesis: Finding Layer-Wise Differentiated Network Architectures

1 code implementation CVPR 2021 Yawei Li, Wen Li, Martin Danelljan, Kai Zhang, Shuhang Gu, Luc van Gool, Radu Timofte

Based on that, we articulate the heterogeneity hypothesis: with the same training protocol, there exists a layer-wise differentiated network architecture (LW-DNA) that can outperform the original network with regular channel configurations but with a lower level of model complexity.

Image Classification Image Restoration +1

Flexible Example-based Image Enhancement with Task Adaptive Global Feature Self-Guided Network

no code implementations13 May 2020 Dario Kneubuehler, Shuhang Gu, Luc van Gool, Radu Timofte

We propose the first practical multitask image enhancement network, that is able to learn one-to-many and many-to-one image mappings.

Image Enhancement

Unsupervised Multimodal Video-to-Video Translation via Self-Supervised Learning

no code implementations14 Apr 2020 Kangning Liu, Shuhang Gu, Andres Romero, Radu Timofte

Existing unsupervised video-to-video translation methods fail to produce translated videos which are frame-wise realistic, semantic information preserving and video-level consistent.

Self-Supervised Learning Translation

Unsupervised Real-world Image Super Resolution via Domain-distance Aware Training

1 code implementation CVPR 2021 Yunxuan Wei, Shuhang Gu, Yawei Li, Longcun Jin

The philosophy of off-the-shelf approaches lies in the augmentation of unpaired data, i. e. first generating synthetic low-resolution (LR) images $\mathcal{Y}^g$ corresponding to real-world high-resolution (HR) images $\mathcal{X}^r$ in the real-world LR domain $\mathcal{Y}^r$, and then utilizing the pseudo pairs $\{\mathcal{Y}^g, \mathcal{X}^r\}$ for training in a supervised manner.

Image Super-Resolution

DHP: Differentiable Meta Pruning via HyperNetworks

2 code implementations ECCV 2020 Yawei Li, Shuhang Gu, Kai Zhang, Luc van Gool, Radu Timofte

Passing the sparsified latent vectors through the hypernetworks, the corresponding slices of the generated weight parameters can be removed, achieving the effect of network pruning.

Denoising Image Classification +3

Group Sparsity: The Hinge Between Filter Pruning and Decomposition for Network Compression

2 code implementations CVPR 2020 Yawei Li, Shuhang Gu, Christoph Mayer, Luc van Gool, Radu Timofte

In this paper, we analyze two popular network compression techniques, i. e. filter pruning and low-rank decomposition, in a unified sense.

Frequency Separation for Real-World Super-Resolution

2 code implementations18 Nov 2019 Manuel Fritsche, Shuhang Gu, Radu Timofte

Furthermore, we propose to separate the low and high image frequencies and treat them differently during training.

Image Super-Resolution

Fast Image Restoration With Multi-Bin Trainable Linear Units

1 code implementation ICCV 2019 Shuhang Gu, Wen Li, Luc Van Gool, Radu Timofte

Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.

Image Denoising Image Restoration +1

Self-Guided Network for Fast Image Denoising

1 code implementation ICCV 2019 Shuhang Gu, Yawei Li, Luc Van Gool, Radu Timofte

During the past years, tremendous advances in image restoration tasks have been achieved using highly complex neural networks.

Image Denoising Image Restoration

Efficient Video Super-Resolution through Recurrent Latent Space Propagation

1 code implementation17 Sep 2019 Dario Fuoli, Shuhang Gu, Radu Timofte

However, as the motion estimation problem is a highly challenging problem, inaccurate motion compensation may affect the performance of VSR algorithms.

Video Super-Resolution Image and Video Processing

Learning Filter Basis for Convolutional Neural Network Compression

3 code implementations ICCV 2019 Yawei Li, Shuhang Gu, Luc van Gool, Radu Timofte

Convolutional neural networks (CNNs) based solutions have achieved state-of-the-art performances for many computer vision tasks, including classification and super-resolution of images.

General Classification Image Classification +2

Exemplar Guided Face Image Super-Resolution without Facial Landmarks

1 code implementation17 Jun 2019 Berk Dogan, Shuhang Gu, Radu Timofte

Nowadays, due to the ubiquitous visual media there are vast amounts of already available high-resolution (HR) face images.

Image Super-Resolution

Learning Content-Weighted Deep Image Compression

1 code implementation1 Apr 2019 Mu Li, WangMeng Zuo, Shuhang Gu, Jane You, David Zhang

Learning-based lossy image compression usually involves the joint optimization of rate-distortion performance.

Image Compression

Multi-bin Trainable Linear Unit for Fast Image Restoration Networks

no code implementations30 Jul 2018 Shuhang Gu, Radu Timofte, Luc van Gool

Tremendous advances in image restoration tasks such as denoising and super-resolution have been achieved using neural networks.

Image Denoising Image Restoration +1

Video Rain Streak Removal by Multiscale Convolutional Sparse Coding

no code implementations CVPR 2018 Minghan Li, Qi Xie, Qian Zhao, Wei Wei, Shuhang Gu, Jing Tao, Deyu Meng

Based on such understanding, we specifically formulate both characteristics into a multiscale convolutional sparse coding (MS-CSC) model for the video rain streak removal task.

Rain Removal

Enlarging Context with Low Cost: Efficient Arithmetic Coding with Trimmed Convolution

no code implementations15 Jan 2018 Mu Li, Shuhang Gu, David Zhang, WangMeng Zuo

One key issue of arithmetic encoding method is to predict the probability of the current coding symbol from its context, i. e., the preceding encoded symbols, which usually can be executed by building a look-up table (LUT).

Image Compression

Joint Convolutional Analysis and Synthesis Sparse Representation for Single Image Layer Separation

no code implementations ICCV 2017 Shuhang Gu, Deyu Meng, WangMeng Zuo, Lei Zhang

To exploit the complementary representation mechanisms of ASR and SSR, we integrate the two models and propose a joint convolutional analysis and synthesis (JCAS) sparse representation model.

Tone Mapping

Learning Dynamic Guidance for Depth Image Enhancement

no code implementations CVPR 2017 Shuhang Gu, WangMeng Zuo, Shi Guo, Yunjin Chen, Chongyu Chen, Lei Zhang

To address these limitations, we propose a weighted analysis representation model for guided depth image enhancement, which advances the conventional methods in two aspects: (i) task driven learning and (ii) dynamic guidance.

Depth Image Upsampling Image Enhancement +1

Learning Deep CNN Denoiser Prior for Image Restoration

1 code implementation CVPR 2017 Kai Zhang, WangMeng Zuo, Shuhang Gu, Lei Zhang

Recent works have revealed that, with the aid of variable splitting techniques, denoiser prior can be plugged in as a modular part of model-based optimization methods to solve other inverse problems (e. g., deblurring).

Color Image Denoising Deblurring +2

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

Multispectral Images Denoising by Intrinsic Tensor Sparsity Regularization

no code implementations CVPR 2016 Qi Xie, Qian Zhao, Deyu Meng, Zongben Xu, Shuhang Gu, WangMeng Zuo, Lei Zhang

Multispectral images (MSI) can help deliver more faithful representation for real scenes than the traditional image system, and enhance the performance of many computer vision tasks.


Weighted Schatten $p$-Norm Minimization for Image Denoising and Background Subtraction

no code implementations3 Dec 2015 Yuan Xie, Shuhang Gu, Yan Liu, WangMeng Zuo, Wensheng Zhang, Lei Zhang

However, NNM tends to over-shrink the rank components and treats the different rank components equally, limiting its flexibility in practical applications.

Image Denoising

Convolutional Sparse Coding for Image Super-Resolution

no code implementations ICCV 2015 Shuhang Gu, WangMeng Zuo, Qi Xie, Deyu Meng, Xiangchu Feng, Lei Zhang

Sparse coding (SC) plays an important role in versatile computer vision applications such as image super-resolution (SR).

Image Reconstruction Image Super-Resolution

Discriminative Learning of Iteration-Wise Priors for Blind Deconvolution

no code implementations CVPR 2015 Wangmeng Zuo, Dongwei Ren, Shuhang Gu, Liang Lin, Lei Zhang

The maximum a posterior (MAP)-based blind deconvolution framework generally involves two stages: blur kernel estimation and non-blind restoration.


Weighted Nuclear Norm Minimization with Application to Image Denoising

no code implementations CVPR 2014 Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng

In this paper we study the weighted nuclear norm minimization (WNNM) problem, where the singular values are assigned different weights.

Image Denoising

On the Optimal Solution of Weighted Nuclear Norm Minimization

no code implementations23 May 2014 Qi Xie, Deyu Meng, Shuhang Gu, Lei Zhang, WangMeng Zuo, Xiangchu Feng, Zongben Xu

Nevertheless, so far the global optimal solution of WNNM problem is not completely solved yet due to its non-convexity in general cases.

Image Denoising

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