Zobrazeno 1 - 10
of 404
pro vyhledávání: '"Low-light enhancement"'
Publikováno v:
ICT Express, Vol 10, Iss 6, Pp 1206-1211 (2024)
The degradation of recognition rates in low-light environments is a critical issue in terms of security when using object and face recognition technologies in various locations. Existing low-light enhancement models have shown limitations in terms of
Externí odkaz:
https://doaj.org/article/5f0748746d60413dbd561da1b1b8da26
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-19 (2024)
Abstract Advancements in digital imaging and video processing are often challenged by low-light environments, leading to degraded visual quality. This affects critical sectors such as medical imaging, aerospace, and underwater exploration, where unev
Externí odkaz:
https://doaj.org/article/d3f534cc301f4ef28af5ef6f0b0c2ce1
Publikováno v:
Frontiers of Optoelectronics, Vol 17, Iss 1, Pp 1-19 (2024)
Abstract Restricted by the lighting conditions, the images captured at night tend to suffer from color aberration, noise, and other unfavorable factors, making it difficult for subsequent vision-based applications. To solve this problem, we propose a
Externí odkaz:
https://doaj.org/article/59c7437411d0476a9f7042bca6c0e542
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-16 (2024)
Abstract Current low-light enhancement algorithms fail to suppress noise when enhancing brightness, and may introduces structural distortion and color distortion caused by halos or artifacts. This paper proposes a content-illumination coupling guided
Externí odkaz:
https://doaj.org/article/c3a5c428d5d341518522683252753acb
Publikováno v:
IEEE Access, Vol 12, Pp 78354-78365 (2024)
Traditional enhancement techniques can improve the contrast of low-light and low-resolution images, but they fail to recover their resolution. Conversely, traditional super-resolution (SR) algorithms can enhance resolution but not restore contrast. T
Externí odkaz:
https://doaj.org/article/4c4f8c6ce8d647aebdd7f9cad666962f
Publikováno v:
IEEE Access, Vol 12, Pp 24071-24078 (2024)
In this paper, we propose an innovative image enhancement algorithm called “Dual-Enhancing-Dense-UNet (DEDUNet)” that simultaneously performs image brightness enhancement and reduces noise. This model is based on Convolutional Neural Network (CNN
Externí odkaz:
https://doaj.org/article/4139fc00836e4ca883cbb4d6c2506f89
Publikováno v:
Sensors, Vol 24, Iss 21, p 6943 (2024)
Coal gangue identification is the primary step in coal flow initial screening, which mainly faces problems such as low identification efficiency, complex algorithms, and high hardware requirements. In response to the above, this article proposes a ne
Externí odkaz:
https://doaj.org/article/d298d95aa77f40be96b08dea176fee96
Publikováno v:
Journal of Cloud Computing: Advances, Systems and Applications, Vol 12, Iss 1, Pp 1-14 (2023)
Abstract With the widespread adoption of mobile multimedia devices, the deployment of compute-intensive inference tasks on edge and resource-constrained devices, particularly in the context of low-light text detection, remains a formidable challenge.
Externí odkaz:
https://doaj.org/article/b9f7218b3dca4cff8d367262d4f72960
Autor:
Anqi Yi, Nantheera Anantrasirichai
Publikováno v:
Sensors, Vol 24, Iss 13, p 4359 (2024)
Accurate object tracking in low-light environments is crucial, particularly in surveillance, ethology applications, and biometric recognition systems. However, achieving this is significantly challenging due to the poor quality of captured sequences.
Externí odkaz:
https://doaj.org/article/58fc2bae82d3476f80670800914c5fb5
Autor:
Xinghao Wang, Qiang Wang, Lei Zhang, Yi Qu, Fan Yi, Jiayang Yu, Qiuhan Liu, Ruicong Xia, Ziling Xu, Sirong Tong
Publikováno v:
Frontiers in Neuroscience, Vol 18 (2024)
The direct utilization of low-light images hinders downstream visual tasks. Traditional low-light image enhancement (LLIE) methods, such as Retinex-based networks, require image pairs. A spiking-coding methodology called intensity-to-latency has been
Externí odkaz:
https://doaj.org/article/ace7382a381d494e876ab81c3dddff04