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

To study the 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 process. This strategy generates 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 "denoising first and demosaicking later" scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. Already in the technique, first decomposed the noisy CFA image into two parts: the low-pass smooth image and high-pass smooth image by using 2D-Gaussian low-pass filter and denoised the high-pass image only and further combine with lowpass image. Not large but least noise component are present in the low-pass image, which can create more noise in the image when it process further (demosaicking). An adaptive denoising algorithm can be used by using principle component analysis (PCA), which works directly on the high-pass as well as low-pass CFA data. Which can give the better result for the denoising.