AIFNet: All-in-Focus Image Restoration Network Using a Light Field-Based Dataset

Defocus blur often degrades the performance of image understanding, such as object recognition and image segmentation. Restoring an all-in-focus image from its defocused version is highly beneficial to visual information processing and many photographic applications, despite being a severely ill-pos...

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Veröffentlicht in:	IEEE transactions on computational imaging Jg. 7; S. 675 - 688
Hauptverfasser:	Ruan, Lingyan, Chen, Bin, Li, Jizhou, Lam, Miu-Ling
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Piscataway IEEE 2021 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Apertures Artificial neural networks Cameras Computer architecture Data processing Datasets deep learning defocus deblurring Estimation Ill posed problems Image deblurring Image edge detection Image restoration Image segmentation Kernel Lenses light field Object recognition Performance degradation Synthetic apertures
ISSN:	2573-0436, 2333-9403
Online-Zugang:	Volltext
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