A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
ExposureNet: Mobile camera exposure parameters autonomous control for blur effect prevention
Tekijät: Nahli, Abdelwahed; Li, Dan; Uddin, Rahim; Irfan, Muhammad; Oubibi, Mohamed; Lu, Qiyong; Zhang, Jian Qiu
Kustantaja: WILEY
Kustannuspaikka: HOBOKEN
Julkaisuvuosi: 2024
Journal: IET Image Processing
Tietokannassa oleva lehden nimi: IET IMAGE PROCESSING
Lehden akronyymi: IET IMAGE PROCESS
Vuosikerta: 18
Numero: 12
Aloitussivu: 3403
Lopetussivu: 3414
Sivujen määrä: 12
ISSN: 1751-9659
eISSN: 1751-9667
DOI: https://doi.org/10.1049/ipr2.13182
Verkko-osoite: https://doi.org/10.1049/ipr2.13182
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/457511907
The quality of images we perceive visually is heavily impacted by the settings used for camera exposure. When these settings are imbalanced, it can result in an undesired prominent phenomenon known as blur effects. To address this problem, an ExposureNet project has been undertaken, which aims to develop an autonomous camera exposure settings control system for blur effects prevention. The proposed ExposureNet model is a CNN/Transformer hybrid neural structure, created and trained in a comprehensive manner to effectively predict the ideal exposure settings based on the semantic features of the scene being captured. This system is designed to learn the necessary steps for processing, such as identifying relevant scene features, using only two camera exposure parameters (shutter speed (SHS) and ISO) as training signals. As a result, this system can associate the semantic features of a scene with the appropriate exposure parameter adjustments, customized to the scene's dynamics and lighting conditions. By simultaneously optimizing all processing steps and bypassing traditional post-processing stages, the proposed system is designed to achieve faster performance, reduced computational cost, and lower power consumption. Experimental results demonstrate that the proposed system significantly outperforms existing methods and achieves cutting-edge performance.The ExposureNet project addresses the issue of image blur caused by imbalanced camera exposure settings, by developing an autonomous system for controlling these settings. The system, trained comprehensively, predicts ideal exposure based on the semantic features of a scene, using only shutter speed and ISO as training signals. This approach leads to faster performance, reduced computational costs, and lower power consumption, with experimental results showing significant improvements over existing methods. image
Ladattava julkaisu This is an electronic reprint of the original article. |  |
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This study was funded by Fudan University and the National Natural Science Foundation of China. The authors express appreciation to their entire laboratory team for engaging discussions and access to the ultra\u2010fast computing resources.
