PCA-Based Spatially Adaptive Denoising of CFA Images for Single-Sensor Digital Cameras

• Published in: IEEE transactions on image processing 2009-04, Vol.18 (4), p.797-812
• Main Authors: Lei Zhang, Lukac, R., Xiaolin Wu, Zhang, D.
• Format: Article
• Language: English
• Subjects: Adaptive denoising; Algorithm design and analysis; Algorithms; Applied sciences; Arrays; Bayer pattern; Color; color filter array (CFA); Colored noise; Degradation; demosaicking; Detection, estimation, filtering, equalization, prediction; Digital cameras; Digital filters; Exact sciences and technology; Image analysis; Image processing; Image sensors; Information, signal and communications theory; Miscellaneous; Noise; Noise generators; Noise reduction; principle component analysis (PCA); Sensors; Signal and communications theory; Signal processing; Signal, noise; Spectral correlation; Studies; Telecommunications and information theory
• ISSN: 1057-7149; 1941-0042
• DOI: 10.1109/TIP.2008.2011384

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. Few denoising schemes that work directly on the CFA images have been presented because of the difficulties arisen from the red, green and blue interlaced mosaic pattern, yet a well designed ldquodenoising first and demosaicking laterrdquo scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. 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, including those sophisticated demosaicking and denoising schemes, in terms of both objective measurement and visual evaluation.