Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras Using Principle Component Analysis

Authors(1) :-Swati Gupta

Single-sensor digital color cameras use a process called color demosaicking to produce full color images from the data captured by a color filter array (CFA). The quality of demosaicked images is degraded due to the sensor noise introduced during the image acquisition process. The conventional solution to combating CFA sensor noise is demosaicking first, followed by a separate denoising processing. This strategy will generate many noise-caused color artifacts in the demosaicking process, which are hard to remove in the denoising process. This paper presents a principle component analysis (PCA) based spatially-adaptive denoising algorithm, which works directly on the CFA data using a supporting window to analyze the local image statistics. By exploiting the spatial and spectral correlations existed in the CFA image, the proposed method can effectively suppress noise while preserving color edges and details. Experiments using both simulated and real CFA images indicate that the proposed scheme outperforms many existing approaches.

Authors and Affiliations

Swati Gupta
Computer Science, Naraina Vidyapeeth Engineering and Mmangement Institute, Kanpur, Uttar Pradesh, India

Adaptive denoising, Bayer pattern, Color Filter Array (CFA), Demosaicking, Principle Component Analysis (PCA).

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Publication Details

Published in : Volume 2 | Issue 3 | May-June 2017
Date of Publication : 2017-06-30
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 560-563
Manuscript Number : CSEIT172389
Publisher : Technoscience Academy

ISSN : 2456-3307

Cite This Article :

Swati Gupta, "Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras Using Principle Component Analysis", International Journal of Scientific Research in Computer Science, Engineering and Information Technology (IJSRCSEIT), ISSN : 2456-3307, Volume 2, Issue 3, pp.560-563, May-June-2017.
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