Abstract
This paper introduces a two-stage image denoising method which consists principal component analysis with local pixel grouping (LPG_PCA) and neighbor embedding (NE). We group local similar patches from input noise image, and then we use PCA technique to analysis the significant component by these patches. After shrinking the coefficient in the PCA domain, most of noise can be suppressed. The denoised image by LPG_PCA and estimated noise level are taken for second stage. In NE algorithm, we find several nearest patches for each input noisy image patch from the noisy training images and compute the reconstruction weights, and then we use the weights to reconstruct the denoised image patches through the support of the noise-free training images. Finally, the experimental results demonstrate our method not only improves the original algorithms but also achieves better denoising performance both the visual quality and the objective criteria such as PSNR and SSIM.