Search Results for author: Zhixun Su

Found 20 papers, 3 papers with code

Unpaired Learning for Deep Image Deraining With Rain Direction Regularizer

no code implementations ICCV 2021 Yang Liu, Ziyu Yue, Jinshan Pan, Zhixun Su

With the estimated rain maps from the semi-supervised learning part, we first synthesize a new paired set by adding to rain-free images based on the superimposition model.

Knowledge Distillation Rain Removal

Joint Self-Attention and Scale-Aggregation for Self-Calibrated Deraining Network

1 code implementation6 Aug 2020 Cong Wang, Yutong Wu, Zhixun Su, Junyang Chen

In the field of multimedia, single image deraining is a basic pre-processing work, which can greatly improve the visual effect of subsequent high-level tasks in rainy conditions.

Single Image Deraining

DCSFN: Deep Cross-scale Fusion Network for Single Image Rain Removal

no code implementations3 Aug 2020 Cong Wang, Xiaoying Xing, Zhixun Su, Junyang Chen

Further, we design an inner-scale connection block to utilize the multi-scale information and features fusion way between different scales to improve rain representation ability and we introduce the dense block with skip connection to inner-connect these blocks.

Rain Removal

SMPR: Single-Stage Multi-Person Pose Regression

1 code implementation28 Jun 2020 Junqi Lin, Huixin Miao, Junjie Cao, Zhixun Su, Risheng Liu

Existing multi-person pose estimators can be roughly divided into two-stage approaches (top-down and bottom-up approaches) and one-stage approaches.

Multi-Person Pose Estimation

Physical Model Guided Deep Image Deraining

no code implementations30 Mar 2020 Honghe Zhu, Cong Wang, Ya-Jie Zhang, Zhixun Su, Guohui Zhao

Single image deraining is an urgent task because the degraded rainy image makes many computer vision systems fail to work, such as video surveillance and autonomous driving.

Autonomous Driving Single Image Deraining

Non-rigid 3D shape retrieval based on multi-view metric learning

no code implementations20 Mar 2019 Haohao Li, Shengfa Wang, Nannan Li, Zhixun Su, Ximin Liu

The different intrinsic representations (features) focus on different geometric properties to describe the same 3D shape, which makes the representations are related.

3D Shape Classification 3D Shape Retrieval +1

Learning Data Terms for Non-blind Deblurring

no code implementations ECCV 2018 Jiangxin Dong, Jinshan Pan, Deqing Sun, Zhixun Su, Ming-Hsuan Yang

We propose a simple and effective discriminative framework to learn data terms that can adaptively handle blurred images in the presence of severe noise and outliers.


Learning Video-Story Composition via Recurrent Neural Network

no code implementations31 Jan 2018 Guangyu Zhong, Yi-Hsuan Tsai, Sifei Liu, Zhixun Su, Ming-Hsuan Yang

In this paper, we propose a learning-based method to compose a video-story from a group of video clips that describe an activity or experience.

Deep Blind Image Inpainting

no code implementations25 Dec 2017 Yang Liu, Jinshan Pan, Zhixun Su

However, directly using exist- ing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions.

Image Inpainting Image Restoration

Blind Image Deblurring With Outlier Handling

no code implementations ICCV 2017 Jiangxin Dong, Jinshan Pan, Zhixun Su, Ming-Hsuan Yang

We analyze the relationship between the proposed algorithm and other blind deblurring methods with outlier handling and show how to estimate intermediate latent images for blur kernel estimation principally.

Blind Image Deblurring Outlier Detection

An Optimization Framework with Flexible Inexact Inner Iterations for Nonconvex and Nonsmooth Programming

no code implementations28 Feb 2017 Yiyang Wang, Risheng Liu, Xiaoliang Song, Zhixun Su

In recent years, numerous vision and learning tasks have been (re)formulated as nonconvex and nonsmooth programmings(NNPs).

Robust Kernel Estimation With Outliers Handling for Image Deblurring

no code implementations CVPR 2016 Jinshan Pan, Zhouchen Lin, Zhixun Su, Ming-Hsuan Yang

Estimating blur kernels from real world images is a challenging problem as the linear image formation assumption does not hold when significant outliers, such as saturated pixels and non-Gaussian noise, are present.

Deblurring Image Restoration

Sparse Coding and Counting for Robust Visual Tracking

no code implementations28 May 2016 Risheng Liu, Jing Wang, Yiyang Wang, Zhixun Su, Yu Cai

In this paper, we propose a novel sparse coding and counting method under Bayesian framwork for visual tracking.

Visual Tracking

Deblurring Text Images via L0-Regularized Intensity and Gradient Prior

no code implementations CVPR 2014 Jinshan Pan, Zhe Hu, Zhixun Su, Ming-Hsuan Yang

We propose a simple yet effective L_0-regularized prior based on intensity and gradient for text image deblurring.

Deblurring Image Restoration

Parallel matrix factorization for low-rank tensor completion

1 code implementation4 Dec 2013 Yangyang Xu, Ruru Hao, Wotao Yin, Zhixun Su

Phase transition plots reveal that our algorithm can recover a variety of synthetic low-rank tensors from significantly fewer samples than the compared methods, which include a matrix completion method applied to tensor recovery and two state-of-the-art tensor completion methods.

Numerical Analysis Numerical Analysis Computation

Kernel Estimation from Salient Structure for Robust Motion Deblurring

no code implementations5 Dec 2012 Jinshan Pan, Risheng Liu, Zhixun Su, Xianfeng GU

One effective way to eliminate these details is to apply image denoising model based on the Total Variation (TV).

Blind Image Deblurring Image Denoising +1

Linearized Alternating Direction Method with Adaptive Penalty for Low-Rank Representation

no code implementations NeurIPS 2011 Zhouchen Lin, Risheng Liu, Zhixun Su

It suffers from $O(n^3)$ computation complexity due to the matrix-matrix multiplications and matrix inversions, even if partial SVD is used.

Optimization and Control

Solving Principal Component Pursuit in Linear Time via $l_1$ Filtering

no code implementations26 Aug 2011 Risheng Liu, Zhouchen Lin, Siming Wei, Zhixun Su

In this paper, we propose a novel algorithm, called $l_1$ filtering, for \emph{exactly} solving PCP with an $O(r^2(m+n))$ complexity, where $m\times n$ is the size of data matrix and $r$ is the rank of the matrix to recover, which is supposed to be much smaller than $m$ and $n$.

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