Results 211 to 220 of about 126,420 (251)

Vegetated landscape coherence and complexity shape psychophysiological stress recovery in campus courtyards. [PDF]

PLoS One
Gao Y, Liu Q, Yang W, Lin L, Huang H.
europepmc   +1 more source

GPRF-HPNet Physics-guided texture-aware fusion for real-world underwater image enhancement. [PDF]

Sci Rep
Xu J, Kiah MLM, Noor RM, Xu L, Xu C, Wu Y, Por LY.   +6 more
europepmc   +1 more source

PaEDNet: A Robust Denoising and Classification Framework for Vibration-Based Fault Diagnosis with Measurement Noise. [PDF]

Sensors (Basel)
Liao X, Chi Y, Bai Y, Dai Q, Zhao P, Li N, Sun L, Li D.   +7 more
europepmc   +1 more source

Impact of biophilic office features on nursing managers' well-being, productivity, and decision-making: a predictive correlational study. [PDF]

BMC Nurs
Esmeil NA, Hossny EK, Mostafa NM, Abdelkader AM, Elhay HAA, Younes HS, Ahmed HM.   +6 more
europepmc   +1 more source

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Strip Attention for Image Restoration

Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, 2023
As a long-standing task, image restoration aims to recover the latent sharp image from its degraded counterpart. In recent years, owing to the strong ability of self-attention in capturing long-range dependencies, Transformer based methods have achieved promising performance on multifarious image restoration tasks. However, the canonical self-attention
Yuning Cui 0001, Yi Tao, Luoxi Jing, Alois Knoll   +3 more
openaire   +1 more source

Synergic Feature Attention for Image Restoration

ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2021
Local and non-local attentions are both effective methods in the domain of image restoration (IR). However, most existing image restoration methods use these two strategies indiscriminately, and how to make a trade-off between local and non-local attention operations has hardly been studied. Furthermore, the commonly used pixel-wise non-local operation
Chong Mou, Jian Zhang 0018
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Learning Deformable and Attentive Network for image restoration

Knowledge-Based Systems, 2021
Abstract Image restoration (IR) aims to recover image quality from various degradations. Existing convolutional neural networks (CNN) based IR methods try to improve performance by enlarging the model receptive field with the sacrifice of fine spatial details and extra artifacts.
Yuan Huang, Xingsong Hou, Yujie Dun, Jie Qin 0004, Li Liu 0004, Xueming Qian, Ling Shao 0001   +6 more
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Attention and Restoration in Post-RN Students

The Journal of Continuing Education in Nursing, 2005
ABSTRACT Background: The impact of a restorative intervention using the natural environment on capacity to direct attention and issues that contribute to attention fatigue for diploma-prepared nursing students (Post-RN students) enrolled in a baccalaureate nursing program was examined.
Christina M, Sanders, Dawn, Yankou, Mary-Anne, Andrusyszyn   +2 more
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Mix-order Attention Networks for Image Restoration

Proceedings of the 29th ACM International Conference on Multimedia, 2021
Convolutional neural networks (CNNs) have obtained great success in image restoration tasks, like single image denoising, demosaicing, and super-resolution. However, most existing CNN-based methods neglect the diversity of image contents and degradations in the corrupted images and treat channel-wise features equally, thus hindering the representation ...
Tao Dai 0001, Yalei Lv, Bin Chen 0011, Zhi Wang 0001, Zexuan Zhu, Shu-Tao Xia   +5 more
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