Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Hu Runze"'
Publikováno v:
IEEE Photonics Journal, Vol 16, Iss 2, Pp 1-10 (2024)
In recent years, with the continuous progress of technology and the development of society, the demand for updating trace gas detection technology has been increasing. The ability to quickly and accurately detect the composition and concentration of
Externí odkaz:
https://doaj.org/article/aaa4937beecc4b26af2e4d7839fb183e
Image Quality Assessment (IQA) remains an unresolved challenge in the field of computer vision, due to complex distortion conditions, diverse image content, and limited data availability. The existing Blind IQA (BIQA) methods heavily rely on extensiv
Externí odkaz:
http://arxiv.org/abs/2409.05381
Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) aim to simulate human subjective perception of image visual quality and aesthetic appeal. Existing methods typically address these tasks independently due to distinct learning object
Externí odkaz:
http://arxiv.org/abs/2406.01069
Autor:
Pan, Wensheng, Gao, Timin, Zhang, Yan, Hu, Runze, Zheng, Xiawu, Zhang, Enwei, Gao, Yuting, Liu, Yutao, Shen, Yunhang, Li, Ke, Zhang, Shengchuan, Cao, Liujuan, Ji, Rongrong
Image Quality Assessment (IQA) models benefit significantly from semantic information, which allows them to treat different types of objects distinctly. Currently, leveraging semantic information to enhance IQA is a crucial research direction. Tradit
Externí odkaz:
http://arxiv.org/abs/2404.14949
Storing intermediate frame segmentations as memory for long-range context modeling, spatial-temporal memory-based methods have recently showcased impressive results in semi-supervised video object segmentation (SVOS). However, these methods face two
Externí odkaz:
http://arxiv.org/abs/2403.11529
Autor:
Li, Xudong, Zheng, Jingyuan, Hu, Runze, Zhang, Yan, Li, Ke, Shen, Yunhang, Zheng, Xiawu, Liu, Yutao, Zhang, ShengChuan, Dai, Pingyang, Ji, Rongrong
Blind Image Quality Assessment (BIQA) aims to evaluate image quality in line with human perception, without reference benchmarks. Currently, deep learning BIQA methods typically depend on using features from high-level tasks for transfer learning. Ho
Externí odkaz:
http://arxiv.org/abs/2401.11949
Concealed object segmentation (COS) is a challenging task that involves localizing and segmenting those concealed objects that are visually blended with their surrounding environments. Despite achieving remarkable success, existing COS segmenters sti
Externí odkaz:
http://arxiv.org/abs/2401.11767
Autor:
Li, Xudong, Gao, Timin, Hu, Runze, Zhang, Yan, Zhang, Shengchuan, Zheng, Xiawu, Zheng, Jingyuan, Shen, Yunhang, Li, Ke, Liu, Yutao, Dai, Pingyang, Ji, Rongrong
The current state-of-the-art No-Reference Image Quality Assessment (NR-IQA) methods typically rely on feature extraction from upstream semantic backbone networks, assuming that all extracted features are relevant. However, we make a key observation t
Externí odkaz:
http://arxiv.org/abs/2312.06158
Autor:
Li, Xudong, Zheng, Jingyuan, Zheng, Xiawu, Hu, Runze, Zhang, Enwei, Gao, Yuting, Shen, Yunhang, Li, Ke, Liu, Yutao, Dai, Pingyang, Zhang, Yan, Ji, Rongrong
Image Quality Assessment (IQA) with reference images have achieved great success by imitating the human vision system, in which the image quality is effectively assessed by comparing the query image with its pristine reference image. However, for the
Externí odkaz:
http://arxiv.org/abs/2312.00591
Underwater image enhancement (UIE) poses challenges due to distinctive properties of the underwater environment, including low contrast, high turbidity, visual blurriness, and color distortion. In recent years, the application of deep learning has qu
Externí odkaz:
http://arxiv.org/abs/2311.00246