Pseudo four-channel image denoising for noisy CFA raw data

Most demosaicking algorithms only focus on handling noise-free CFA raw data. In practice, the CFA raw data are corrupted by noise, which degrades demosaicking performance. Full-color image quality strongly depends on the performance of the demosaicking. Here, we propose a CFA raw data denoising algorithm. In the proposed algorithm, the CFA raw data is converted to a pseudo four-channel image by rearranging pixels. Then, the four-channel data are transformed based on the principal component analysis (PCA). Existing high-performance gray image denoising algorithm is applied to each transformed image. Finally, the denoised data is rearranged to obtain denoised CFA raw data. We evaluate both the denoised CFA raw data as well as the full-color image reconstructed with the noisy CFA raw data. Experimental comparisons demonstrate that the proposed algorithm outperforms existing state-of-the-art algorithms.