68 citations · 79 across the 10 of their papers we have counts for
12 papers
CuPIQP: A Batched and Differentiable QP Solver on GPUs for Robot Learning and Control
Fenglong Song, Roland Schwan, Yuwen Chen +2
Quadratic programs (QPs) are fundamental building blocks of many robotic tasks such as inverse kinematics, whole-body control, and model predictive control. Extending these methods…
Accelerating Branch MPC with Two-Level Parallel Direct Solves on GPUs
Fenglong Song, Luyao Zhang, Liang Wu +2
Branch model predictive control optimizes multiple future trajectories coupled through shared decisions, with computational demands increasing as the number of scenarios and predic…
Parallel KKT Solver in PIQP for Multistage Optimization
Fenglong Song, Roland Schwan, Yuwen Chen +1
This paper presents an efficient parallel Cholesky factorization and triangular solve algorithm for the Karush-Kuhn-Tucker (KKT) systems arising in multistage optimization problems…
Towards Real World HDRTV Reconstruction: A Data Synthesis-based Approach
Zhen Cheng, Tao Wang, Yong Li +3
Existing deep learning based HDRTV reconstruction methods assume one kind of tone mapping operators (TMOs) as the degradation procedure to synthesize SDRTV-HDRTV pairs for supervis…
Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
Marcos V. Conde, Radu Timofte, Yibin Huang +40
Cameras capture sensor RAW images and transform them into pleasant RGB images, suitable for the human eyes, using their integrated Image Signal Processor (ISP). Numerous low-level…
SJ-HD^2R: Selective Joint High Dynamic Range and Denoising Imaging for Dynamic Scenes
Wei Li, Shuai Xiao, Tianhong Dai +4
Ghosting artifacts, motion blur, and low fidelity in highlight are the main challenges in High Dynamic Range (HDR) imaging from multiple Low Dynamic Range (LDR) images. These issue…
Real-time Image Enhancer via Learnable Spatial-aware 3D Lookup Tables
Tao Wang, Yong Li, Jingyang Peng +4
Recently, deep learning-based image enhancement algorithms achieved state-of-the-art (SOTA) performance on several publicly available datasets. However, most existing methods fail…
Fast Camera Image Denoising on Mobile GPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Kim Byeoung-su, Radu Timofte +29
Image denoising is one of the most critical problems in mobile photo processing. While many solutions have been proposed for this task, they are usually working with synthetic data…
Learned Smartphone ISP on Mobile NPUs with Deep Learning, Mobile AI 2021 Challenge: Report
Andrey Ignatov, Cheng-Ming Chiang, Hsien-Kai Kuo +38
As the quality of mobile cameras starts to play a crucial role in modern smartphones, more and more attention is now being paid to ISP algorithms used to improve various perceptual…
NTIRE 2021 Challenge on Quality Enhancement of Compressed Video: Methods and Results
Ren Yang, Radu Timofte, Jing Liu +69
This paper reviews the first NTIRE challenge on quality enhancement of compressed video, with a focus on the proposed methods and results. In this challenge, the new Large-scale Di…
Efficient Multi-Stage Video Denoising with Recurrent Spatio-Temporal Fusion
Matteo Maggioni, Yibin Huang, Cheng Li +3
In recent years, denoising methods based on deep learning have achieved unparalleled performance at the cost of large computational complexity. In this work, we propose an Efficien…
NODE: Extreme Low Light Raw Image Denoising using a Noise Decomposition Network
Hao Guan, Liu Liu, Sean Moran +2
Denoising extreme low light images is a challenging task due to the high noise level. When the illumination is low, digital cameras increase the ISO (electronic gain) to amplify th…