|
We propose a novel principal component analysis (PCA)-based image denoising framework motivated by the fact that the PCA along with patch groups (PGs) can produce better denoising performance. The PGs essentially capture the geometric information from noisy image. In the denoising stage, the learned PGs are transformed to PCA domain and it only preserves the important principal components while removing the noise components. The proposed denoising method consists of mainly three stages such as patch grouping stage, dictionary learning stage and PCA-based denoising stage. Also, we present an algorithm Learned-PGPCA and tested it in a simulated environment. The experimental results divulged that the proposed denoising framework Learned-PGPCA achieved very competitive denoising performance, particularly in preserving edges and textures as compared to recent patch-based image denoising methods pertaining to gaussian noise.
|
