Spatially adaptive image restoration method using LPG-PCA and JBF

This paper presents an efficient image restoration scheme with the help of Principal Component Analysis (PCA) with local pixel grouping (LPG) and Joint Bilateral Filter (JBF) in spatial domain and it also helps to preserve the image local structures. In LPG-PCA method, a vector variable is modeled by using a pixel and its nearest neighbors and also training samples are extracted using the local window and block matching based LPG. It also helps to preserve image local features after coefficient shrinkage in the PCA domain while eliminating noise. For further improvement, the same procedure is iterated again and the noise level is decreased in the second stage. In the third stage, the LPG-PCA output is used as a reference image for the Joint Bilateral Filter (JBF) to preserve and enhance the edges effectively. Experimental results shows that the proposed method gains very competitive denoising performance in terms of PSNR and also the fine structures in an image are preserved. The visual quality shows that this proposed method shows better performance when compare to other methods in reducing various types of noise.