Zobrazeno 1 - 10
of 30
pro vyhledávání: '"Yang, Runzhao"'
Autor:
Yang, Runzhao, Chen, Yinda, Zhang, Zhihong, Liu, Xiaoyu, Li, Zongren, He, Kunlun, Xiong, Zhiwei, Suo, Jinli, Dai, Qionghai
In the field of medical image compression, Implicit Neural Representation (INR) networks have shown remarkable versatility due to their flexible compression ratios, yet they are constrained by a one-to-one fitting approach that results in lengthy enc
Externí odkaz:
http://arxiv.org/abs/2405.16850
Motion information from 4D medical imaging offers critical insights into dynamic changes in patient anatomy for clinical assessments and radiotherapy planning and, thereby, enhances the capabilities of 3D image analysis. However, inherent physical an
Externí odkaz:
http://arxiv.org/abs/2405.15385
Functional Magnetic Resonance Imaging (fMRI) data is a widely used kind of four-dimensional biomedical data, which requires effective compression. However, fMRI compressing poses unique challenges due to its intricate temporal dynamics, low signal-to
Externí odkaz:
http://arxiv.org/abs/2312.00082
The demand for compact cameras capable of recording high-speed scenes with high resolution is steadily increasing. However, achieving such capabilities often entails high bandwidth requirements, resulting in bulky, heavy systems unsuitable for low-ca
Externí odkaz:
http://arxiv.org/abs/2311.13134
Solving partial differential equations (PDEs) has been a fundamental problem in computational science and of wide applications for both scientific and engineering research. Due to its universal approximation property, neural network is widely used to
Externí odkaz:
http://arxiv.org/abs/2305.10033
Autor:
Cheng, Yuxiao, Yang, Runzhao, Xiao, Tingxiong, Li, Zongren, Suo, Jinli, He, Kunlun, Dai, Qionghai
Publikováno v:
The Eleventh International Conference on Learning Representations, Feb. 2023
Causal discovery from time-series data has been a central task in machine learning. Recently, Granger causality inference is gaining momentum due to its good explainability and high compatibility with emerging deep neural networks. However, most exis
Externí odkaz:
http://arxiv.org/abs/2302.07458
Imaging and perception in photon-limited scenarios is necessary for various applications, e.g., night surveillance or photography, high-speed photography, and autonomous driving. In these cases, cameras suffer from low signal-to-noise ratio, which de
Externí odkaz:
http://arxiv.org/abs/2301.06269
Implicit neural representation (INR) can describe the target scenes with high fidelity using a small number of parameters, and is emerging as a promising data compression technique. However, limited spectrum coverage is intrinsic to INR, and it is no
Externí odkaz:
http://arxiv.org/abs/2211.06689
Autor:
Yang, Runzhao, Xiao, Tingxiong, Cheng, Yuxiao, Cao, Qianni, Qu, Jinyuan, Suo, Jinli, Dai, Qionghai
Massive collection and explosive growth of biomedical data, demands effective compression for efficient storage, transmission and sharing. Readily available visual data compression techniques have been studied extensively but tailored for natural ima
Externí odkaz:
http://arxiv.org/abs/2209.15180
Publikováno v:
Information Fusion Volume 93, May 2023, Pages 429-440
Videos captured under low light conditions suffer from severe noise. A variety of efforts have been devoted to image/video noise suppression and made large progress. However, in extremely dark scenarios, extensive photon starvation would hamper preci
Externí odkaz:
http://arxiv.org/abs/2204.04987