Image noise estimation and removal considering the bayer pattern of noise variance
Traditional image denoising methods are designed for Gaussian or Poisson noise, which are not suitable for realistic noise introduced in the complicated imaging pipeline. We observe that, due to the demosaicing process in imaging, the noise variance maps of captured JPEG images are characterized by Bayer patterns. In this paper, we propose a novel noise estimation and removal method based on the Bayer pattern of noise variance maps. There are two key contributions in the proposed method. First, to the best of our knowledge, we are the first to consider the Bayer patterns of noise variance maps in noise estimation and denoising. Second, we extend the state-of-the-art denoising method CBM3D to deal with realistic noise by integrating the estimated noise variance map and Bayer-pattern down-sampling into the denoising process. Experimental results show that the proposed method achieves the best noise estimation performance compared with two state-of-the-art methods. In addition, the denoising performance of CBM3D for realistic noise is significantly improved using the proposed approach and outperforms state-of-the-art blind denoising methods.