Further Improvement of Debayering Performance of RGBW Color Filter Arrays Using Deep Learning and Pansharpening Techniques

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Bibliographische Detailangaben
Zeitschriftentitel:	Journal of Imaging
Personen und Körperschaften:	Kwan, Chou
In:	Journal of Imaging, 5, 2019, 8, S. 68
Format:	E-Article
Sprache:	Englisch
veröffentlicht:	MDPI AG
Schlagwörter:	Electrical and Electronic Engineering Computer Graphics and Computer-Aided Design Computer Vision and Pattern Recognition Radiology, Nuclear Medicine and imaging

Details
Zusammenfassung:	<jats:p>The RGBW color filter arrays (CFA), also known as CFA2.0, contains R, G, B, and white (W) pixels. It is a 4 × 4 pattern that has 8 white pixels, 4 green pixels, 2 red pixels, and 2 blue pixels. The pattern repeats itself over the whole image. In an earlier conference paper, we cast the demosaicing process for CFA2.0 as a pansharpening problem. That formulation is modular and allows us to insert different pansharpening algorithms for demosaicing. New algorithms in interpolation and demosaicing can also be used. In this paper, we propose a new enhancement of our earlier approach by integrating a deep learning-based algorithm into the framework. Extensive experiments using IMAX and Kodak images clearly demonstrated that the new approach improved the demosaicing performance even further.</jats:p>
Umfang:	68
ISSN:	2313-433X
DOI:	10.3390/jimaging5080068