Enhanced Images using Noise Removal with Image Restoration

There are many noise reduction techniques have been developed for removing noise and retaining edge details in images.Each technique has its own assumptions, advantages and limitations. The idea behind using noise removal techniques is to give better results in terms of quality and in removal of different noises.Wavelet approach for noise removal has been successfully exploited by several in the past few decades.Wavelets successfully removes noise while preserving the signal characteristics, regardless of its frequency content.Image denoising is one of the fundamental challenges in the field of image processing and computer vision, where the underlying goal is to estimate the original image by suppressing noise from a noise-contaminated version of the image. Principal component analysis (PCA) is an orthogonal transformation that seeks the Directions of maximum variance in the data and is commonly used to reduce the dimensionality of the data.This disseratation proposes a denoising technique by using a new statistical approach,principal component analysis (PCA) with Local Pixel Grouping.This procedure is iterated second time to further improve the denoising performance and the noise level is adjusted in second stage. Abbreviations- Princial Component Analysis(PCA);Local Pixel Grouping(LPG);Mean Squre Error(MSE);Peak Signal to Noise Ratio(PSNR);Discrete Cosine Transform (DCT);Discrete Wavelet Transform(DWT);Non- Local Means (NLM),Aditive White Gaussian Noise(AWGN).