NDLI: Joint Demosaicing and Denoising via Learned Nonparametric Random Fields

Content Provider	IEEE Xplore Digital Library
Author	Khashabi, D. Nowozin, S. Jancsary, J. Fitzgibbon, A.W.
Copyright Year	1992
Abstract	We introduce a machine learning approach to demosaicing, the reconstruction of color images from incomplete color filter array samples. There are two challenges to overcome by a demosaicing method: 1) it needs to model and respect the statistics of natural images in order to reconstruct natural looking images and 2) it should be able to perform well in the presence of noise. To facilitate an objective assessment of current methods, we introduce a public ground truth data set of natural images suitable for research in image demosaicing and denoising. We then use this large data set to develop a machine learning approach to demosaicing. Our proposed method addresses both demosaicing challenges by learning a statistical model of images and noise from hundreds of natural images. The resulting model performs simultaneous demosaicing and denoising. We show that the machine learning approach has a number of benefits: 1) the model is trained to directly optimize a user-specified performance measure such as peak signal-to-noise ratio (PSNR) or structural similarity; 2) we can handle novel color filter array layouts by retraining the model on such layouts; and 3) it outperforms the previous state-of-the-art, in some setups by 0.7-dB PSNR, faithfully reconstructing edges, textures, and smooth areas. Our results demonstrate that in demosaicing and related imaging applications, discriminatively trained machine learning models have the potential for peak performance at comparatively low engineering effort.
Sponsorship	IEEE Signal Processing Society
Starting Page	4968
Ending Page	4981
Page Count	14
File Size	4625055
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	12
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Image color analysis Noise Noise reduction Arrays Image edge detection Interpolation Cameras regression tree fields Demosaicing denoising
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

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