no code implementations • ECCV 2020 • Liang Chen, Faming Fang, Jiawei Zhang, Jun Liu, Guixu Zhang
Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process.
no code implementations • ECCV 2020 • Liang Chen, Faming Fang, Shen Lei, Fang Li, Guixu Zhang
Specifically, we use a weighted combination of a dense function (i. e. l2) and a newly designed enhanced sparse model termed as le, which is developed from two sparse models (i. e. l1 and l0), to fulfill the task.
no code implementations • 9 Oct 2023 • Pengcheng Lei, Zaoming Yan, Tingting Wang, Faming Fang, Guixu Zhang
Besides, experiments on real-world blurry videos also indicate the good generalization ability of our model.
1 code implementation • 3 Sep 2023 • Pengcheng Lei, Faming Fang, Guixu Zhang, Ming Xu
We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks.
no code implementations • 10 Aug 2023 • Liang Chen, Jiawei Zhang, Zhenhua Li, Yunxuan Wei, Faming Fang, Jimmy Ren, Jinshan Pan
In this paper, we develop a data-driven approach to model the saturated pixels by a learned latent map.
1 code implementation • ICCV 2023 • Pengcheng Lei, Faming Fang, Guixu Zhang, Tieyong Zeng
We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component.
no code implementations • ICCV 2023 • Yuhui Dai, Junkang Zhang, Faming Fang, Guixu Zhang
Utilizing the property that there is a large amount of non-local common characteristics in depth images, we propose a novel model-guided depth recovery method, namely the DC-NLAR model.
no code implementations • 22 Nov 2022 • Pengcheng Lei, Faming Fang, Guixu Zhang
Under the guidance of the flow priors learned in step one, the deformation step designs a pyramid deformable compensation network to compensate for the missing details of the flow step.
no code implementations • 29 Apr 2022 • Juncheng Li, Hanhui Yang, Qiaosi Yi, Faming Fang, Guangwei Gao, Tieyong Zeng, Guixu Zhang
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
no code implementations • 30 Nov 2021 • Qiaosi Yi, Jinhao Liu, Le Hu, Faming Fang, Guixu Zhang
Therefore, we propose a Spatial and Fourier Layer (SFL) to simultaneously learn the local and global information in Spatial and Fourier domains.
1 code implementation • ICCV 2021 • Qiaosi Yi, Juncheng Li, Qinyan Dai, Faming Fang, Guixu Zhang, Tieyong Zeng
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
no code implementations • CVPR 2021 • Liang Chen, Jiawei Zhang, Songnan Lin, Faming Fang, Jimmy S. Ren
To address this problem, we introduce a new blur model to fit both saturated and unsaturated pixels, and all informative pixels can be considered during deblurring process.
no code implementations • CVPR 2021 • Liang Chen, Jiawei Zhang, Jinshan Pan, Songnan Lin, Faming Fang, Jimmy S. Ren
Deblurring night blurry images is difficult, because the common-used blur model based on the linear convolution operation does not hold in this situation due to the influence of saturated pixels.
1 code implementation • 2 Jun 2021 • Qinyan Dai, Juncheng Li, Qiaosi Yi, Faming Fang, Guixu Zhang
Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.
no code implementations • 24 Feb 2021 • Qiaosi Yi, Juncheng Li, Faming Fang, Aiwen Jiang, Guixu Zhang
To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.
no code implementations • 2 Nov 2020 • Zhihao Gu, Fang Li, Faming Fang, and Guixu Zhang
The proposed method is more flexible in controlling the reg- ularization extent than the existing integer-order regularization methods.
1 code implementation • 30 Aug 2020 • Juncheng Li, Faming Fang, Jiaqian Li, Kangfu Mei, Guixu Zhang
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
1 code implementation • NeurIPS 2019 • Hao Zheng, Faming Fang, Guixu Zhang
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging.
1 code implementation • ECCV 2018 • Juncheng Li, Faming Fang, Kangfu Mei, Guixu Zhang
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).