Search Results for author: Jimmy S. Ren

Found 15 papers, 7 papers with code

Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline

1 code implementation7 May 2019 Guocheng Qian, Yuanhao Wang, Jinjin Gu, Chao Dong, Wolfgang Heidrich, Bernard Ghanem, Jimmy S. Ren

In this work, we comprehensively study the effects of pipelines on the mixture problem of learning-based DN, DM, and SR, in both sequential and joint solutions.

Demosaicking Denoising +1

Generalizing Monocular 3D Human Pose Estimation in the Wild

1 code implementation11 Apr 2019 Luyang Wang, Yan Chen, Zhenhua Guo, Keyuan Qian, Mude Lin, Hongsheng Li, Jimmy S. Ren

We observe that recent innovation in this area mainly focuses on new techniques that explicitly address the generalization issue when using this dataset, because this database is constructed in a highly controlled environment with limited human subjects and background variations.

3D Pose Estimation Monocular 3D Human Pose Estimation

Bringing Events Into Video Deblurring With Non-Consecutively Blurry Frames

1 code implementation ICCV 2021 Wei Shang, Dongwei Ren, Dongqing Zou, Jimmy S. Ren, Ping Luo, WangMeng Zuo

EFM can also be easily incorporated into existing deblurring networks, making event-driven deblurring task benefit from state-of-the-art deblurring methods.

Deblurring

Training Weakly Supervised Video Frame Interpolation With Events

1 code implementation ICCV 2021 ZHIYANG YU, Yu Zhang, Deyuan Liu, Dongqing Zou, Xijun Chen, Yebin Liu, Jimmy S. Ren

Though trained on low frame-rate videos, our framework outperforms existing models trained with full high frame-rate videos (and events) on both GoPro dataset and a new real event-based dataset.

Video Frame Interpolation

EfficientFCN: Holistically-guided Decoding for Semantic Segmentation

no code implementations ECCV 2020 Jianbo Liu, Junjun He, Jiawei Zhang, Jimmy S. Ren, Hongsheng Li

State-of-the-art semantic segmentation algorithms are mostly based on dilated Fully Convolutional Networks (dilatedFCN), which adopt dilated convolutions in the backbone networks to extract high-resolution feature maps for achieving high-performance segmentation performance.

Segmentation Semantic Segmentation

Blind Deblurring for Saturated Images

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.

Deblurring

Learning a Non-Blind Deblurring Network for Night Blurry Images

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.

Deblurring Image Restoration

NTIRE 2022 Challenge on Perceptual Image Quality Assessment

no code implementations23 Jun 2022 Jinjin Gu, Haoming Cai, Chao Dong, Jimmy S. Ren, Radu Timofte

This challenge is divided into two tracks, a full-reference IQA track similar to the previous NTIRE IQA challenge and a new track that focuses on the no-reference IQA methods.

Image Quality Assessment Image Restoration

Self-Supervised Intensity-Event Stereo Matching

no code implementations1 Nov 2022 Jinjin Gu, Jinan Zhou, Ringo Sai Wo Chu, Yan Chen, Jiawei Zhang, Xuanye Cheng, Song Zhang, Jimmy S. Ren

Event cameras are novel bio-inspired vision sensors that output pixel-level intensity changes in microsecond accuracy with a high dynamic range and low power consumption.

Self-Supervised Learning Stereo Matching

Range-Nullspace Video Frame Interpolation With Focalized Motion Estimation

no code implementations CVPR 2023 ZHIYANG YU, Yu Zhang, Dongqing Zou, Xijun Chen, Jimmy S. Ren, Shunqing Ren

Continuous-time video frame interpolation is a fundamental technique in computer vision for its flexibility in synthesizing motion trajectories and novel video frames at arbitrary intermediate time steps.

Video Frame Interpolation

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