Zobrazeno 1 - 10
of 2 509
pro vyhledávání: '"Image dehazing"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-17 (2024)
Abstract In the realm of deep learning-based networks for dehazing using paired clean-hazy image datasets to address complex real-world haze scenarios in daytime environments and cross-dataset challenges remains a significant concern due to algorithm
Externí odkaz:
https://doaj.org/article/d9755567ae5e4964b6f55170b43cf116
Publikováno v:
大数据, Vol 10, Pp 77-88 (2024)
To enhance image clarity and address the difficulties in feature extraction and incomplete haze removal in traditional image dehazing processes, a multi-feature fusion based generative adversarial dehazing network is proposed.The network adopts a gen
Externí odkaz:
https://doaj.org/article/6f9509c6bc3845ecbd3960cc3d03b8a3
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-11 (2024)
Abstract Artificially extracted agricultural phenotype information exhibits high subjectivity and low accuracy, while the utilization of image extraction information is susceptible to interference from haze. Furthermore, the effectiveness of the agri
Externí odkaz:
https://doaj.org/article/684a0bc6927d48ce97b980ef95853a57
Autor:
M. Pavethra, M. Uma Devi
Publikováno v:
Automatika, Vol 65, Iss 3, Pp 1139-1153 (2024)
The current version of imaging equipment cannot quickly and effectively make up for the reduction of visibility triggered by bad weather. Traditional strategies minimize hazy impacts by employing an image depth model and a physical model. Following e
Externí odkaz:
https://doaj.org/article/5d257435fed544d8957fb64f1c5c4853
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 5, Pp 1182-1196 (2024)
As a fundamental computer vision task, image dehazing aims to preprocess degraded images by restoring color contrast and texture information to improve visibility and image quality, thereby the clear images can be recovered for subsequent high-level
Externí odkaz:
https://doaj.org/article/534432080221416aaf532e75ca593b58
Publikováno v:
Meitan xuebao, Vol 49, Iss 4, Pp 2167-2178 (2024)
Due to the influence of dust, water mist and low illumination environment in coal mine, it is very difficult to accurately identify the monitoring images of belt transportation system. Aiming at the problem of poor image processing results and effici
Externí odkaz:
https://doaj.org/article/e0da1408f5184bd9835b34288a366f25
Publikováno v:
Ain Shams Engineering Journal, Vol 15, Iss 8, Pp 102861- (2024)
Single image dehazing is a fundamental but challenging task in image processing. Various deep learning-based methods have achieved great dehazing performance. However, there are still hazy residues, even color distortion and texture loss when removin
Externí odkaz:
https://doaj.org/article/21c8262f14b340269d82495f1ab11005
Publikováno v:
IEEE Access, Vol 12, Pp 70160-70169 (2024)
Images captured in hazy scenes exhibit severe degradation. Various dehazing methods have been proposed recently. However, most of them have the drawback of color cast in the dehazing results. To solve this problem, we proposed an image dehazing algor
Externí odkaz:
https://doaj.org/article/dc4de85016c149cc9fb631f66d970b39
Publikováno v:
Mathematics, Vol 12, Iss 23, p 3650 (2024)
Remote sensing image dehazing (RSID) aims to remove haze from remote sensing images to enhance their quality. Although existing deep learning-based dehazing methods have made significant progress, it is still difficult to completely remove the uneven
Externí odkaz:
https://doaj.org/article/1d240956681f4a7782dc282c8e650af6
Publikováno v:
Mathematics, Vol 12, Iss 22, p 3553 (2024)
Underwater image restoration is a crucial task in various computer vision applications, including underwater target detection and recognition, autonomous underwater vehicles, underwater rescue, marine organism monitoring, and marine geological survey
Externí odkaz:
https://doaj.org/article/db7958327d134c069b10691f55679dbc