Most existing Digital Color Cameras use a Single-sensor with a color filter array (CFA) to capture images. 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 “denoising first and demosaicking later ” scheme can have advantages such as less noise-caused color artifacts and cost-effective implementation. Lie Zahng et al. has extended and developed a method called PCA-based denoising method on CFA image denoising . However, when noise level is high, accurate estimation of the PCA basis is not possible and the image denoising performance is decreased. The weaknesses of PCA method are the high computational burden they enforce to processors and the more time they need. This paper proposes a new procedure that is improved block matching and 3-D filtering (BM3D) image denoising algorithm. Using an improved version of BM3D, it is possible to achieve a better performance than directly using of BM3D algorithm in a variety of noise levels. This method changes BM3D Algorithm parameter values according to noise level, removes pre-filtering, which is used in high noise level; therefore Peak Signal-to-Noise Ratio (PSNR) and visual quality get improved, and BM3D complexities and processing time are reduced. This improved BM3D algorithm is used to denoise color filter array (CFA) images
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