1 code implementation • CVPR 2023 • Yichen Guo, Mai Xu, Lai Jiang, Leonid Sigal, Yunjin Chen
To alleviate this issue, we propose the first attempt at 360deg image rescaling, which refers to downscaling a 360deg image to a visually valid low-resolution (LR) counterpart and then upscaling to a high-resolution (HR) 360deg image given the LR variant.
2 code implementations • 20 Apr 2022 • Ren Yang, Radu Timofte, Meisong Zheng, Qunliang Xing, Minglang Qiao, Mai Xu, Lai Jiang, Huaida Liu, Ying Chen, Youcheng Ben, Xiao Zhou, Chen Fu, Pei Cheng, Gang Yu, Junyi Li, Renlong Wu, Zhilu Zhang, Wei Shang, Zhengyao Lv, Yunjin Chen, Mingcai Zhou, Dongwei Ren, Kai Zhang, WangMeng Zuo, Pavel Ostyakov, Vyal Dmitry, Shakarim Soltanayev, Chervontsev Sergey, Zhussip Magauiya, Xueyi Zou, Youliang Yan, Pablo Navarrete Michelini, Yunhua Lu, Diankai Zhang, Shaoli Liu, Si Gao, Biao Wu, Chengjian Zheng, Xiaofeng Zhang, Kaidi Lu, Ning Wang, Thuong Nguyen Canh, Thong Bach, Qing Wang, Xiaopeng Sun, Haoyu Ma, Shijie Zhao, Junlin Li, Liangbin Xie, Shuwei Shi, Yujiu Yang, Xintao Wang, Jinjin Gu, Chao Dong, Xiaodi Shi, Chunmei Nian, Dong Jiang, Jucai Lin, Zhihuai Xie, Mao Ye, Dengyan Luo, Liuhan Peng, Shengjie Chen, Qian Wang, Xin Liu, Boyang Liang, Hang Dong, Yuhao Huang, Kai Chen, Xingbei Guo, Yujing Sun, Huilei Wu, Pengxu Wei, Yulin Huang, Junying Chen, Ik Hyun Lee, Sunder Ali Khowaja, Jiseok Yoon
This challenge includes three tracks.
2 code implementations • 2 Mar 2022 • Zhilu Zhang, Ruohao Wang, Hongzhi Zhang, Yunjin Chen, WangMeng Zuo
For this purpose, we take the telephoto image instead of an additional high-resolution image as the supervision information and select a center patch from it as the reference to super-resolve the corresponding short-focus image patch.
no code implementations • 17 Jul 2018 • Peng Qiao, Yong Dou, Yunjin Chen, Wensen Feng
On the contrary, the regularization term learned via discriminative approaches are usually trained for a specific image restoration problem, and fail in the problem for which it is not trained.
no code implementations • 30 Jul 2017 • Hu Chen, Yi Zhang, Yunjin Chen, Junfeng Zhang, Weihua Zhang, Huaiqiaing Sun, Yang Lv, Peixi Liao, Jiliu Zhou, Ge Wang
Compressive sensing (CS) has proved effective for tomographic reconstruction from sparsely collected data or under-sampled measurements, which are practically important for few-view CT, tomosynthesis, interior tomography, and so on.
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.
no code implementations • 24 Feb 2017 • Peng Qiao, Yong Dou, Wensen Feng, Yunjin Chen
In order to preserve the expected property that end-to-end training is available, we exploit the NSS prior by a set of non-local filters, and derive our proposed trainable non-local reaction diffusion (TNLRD) model for image denoising.
no code implementations • 24 Feb 2017 • Wensen Feng, Yunjin Chen
Therefore, in this study we aim to propose an efficient despeckling model with both high computational efficiency and high recovery quality.
no code implementations • 21 Sep 2016 • Wensen Feng, Peng Qiao, Xuanyang Xi, Yunjin Chen
However, in recent two years, discriminatively trained local approaches have started to outperform previous non-local models and have been attracting increasing attentions due to the additional advantage of computational efficiency.
no code implementations • 19 Sep 2016 • Wensen Feng, Hong Qiao, Yunjin Chen
We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR.
21 code implementations • 13 Aug 2016 • Kai Zhang, WangMeng Zuo, Yunjin Chen, Deyu Meng, Lei Zhang
Discriminative model learning for image denoising has been recently attracting considerable attentions due to its favorable denoising performance.
no code implementations • 10 Oct 2015 • Wensen Feng, Yunjin Chen
The degradation of the acquired signal by Poisson noise is a common problem for various imaging applications, such as medical imaging, night vision and microscopy.
no code implementations • 12 Aug 2015 • Yunjin Chen, Thomas Pock
The TNRD approach is applicable for a variety of image restoration tasks by incorporating appropriate reaction force.
Ranked #6 on Color Image Denoising on Darmstadt Noise Dataset
3 code implementations • CVPR 2015 • Yunjin Chen, Wei Yu, Thomas Pock
We propose to train the parameters of the filters and the influence functions through a loss based approach.
no code implementations • 27 Oct 2014 • Yunjin Chen
A trainable filter-based higher-order Markov Random Fields (MRFs) model - the so called Fields of Experts (FoE), has proved a highly effective image prior model for many classic image restoration problems.
no code implementations • 21 Apr 2014 • Yunjin Chen, Wensen Feng, René Ranftl, Hong Qiao, Thomas Pock
The Fields of Experts (FoE) image prior model, a filter-based higher-order Markov Random Fields (MRF) model, has been shown to be effective for many image restoration problems.
no code implementations • 18 Apr 2014 • Peter Ochs, Yunjin Chen, Thomas Brox, Thomas Pock
A rigorous analysis of the algorithm for the proposed class of problems yields global convergence of the function values and the arguments.
no code implementations • 16 Jan 2014 • Yunjin Chen, Thomas Pock, Horst Bischof
We then introduce an approach to learn both analysis operator and synthesis dictionary simultaneously by using a unified framework of bi-level optimization.
no code implementations • 16 Jan 2014 • Yunjin Chen, Thomas Pock, René Ranftl, Horst Bischof
It is now well known that Markov random fields (MRFs) are particularly effective for modeling image priors in low-level vision.
no code implementations • 16 Jan 2014 • Yunjin Chen, René Ranftl, Thomas Pock
Inpainting based image compression approaches, especially linear and non-linear diffusion models, are an active research topic for lossy image compression.
no code implementations • 13 Jan 2014 • Yunjin Chen, René Ranftl, Thomas Pock
Numerical experiments show that our trained models clearly outperform existing analysis operator learning approaches and are on par with state-of-the-art image denoising algorithms.