Sparse Poisson Denoising of Microbial Images Based on Non-local Principal Component Analysis

For the denoising of micro image of microorganism, a Poisson denoising algorithm based on image sparse block representation and dictionary learning was proposed. Firstly, according to the intrinsic correlation of microbial images, the image blocks were clustered by Poisson K-means method; Secondly, the non-local sparse dictionary representation was realized by principal component analysis to complete the intra cluster denoising; Finally, the complete denoised image was obtained by fusion reconstruction. The results show that through sparse block representation and dictionary learning, Poisson can be denoised directly, which can reduce the error of noise model transformation;The improved block and clustering methods can greatly improve the signal-to-noise ratio of denoised image. Compared with other denoising algorithms, the proposed method not only achieves better denoising effect, but also significantly improves the image blur quality, and retains more image details to the maximum extent.