The Image denoising remains a challenge for researchers because noise removal introduces artifacts and causes blurring of the images. Image sensors collect the data sets which are contaminated by noise due to imperfect instruments; disturbed natural phenomenon can also degrade the quality of data of interest. Noise can also be introduced in images due to transmission and compression of images. In this paper we introduce some different type if noise that is Gaussian noise AWGN, Impulsive noise

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... c. So, it is necessary to depute some effective image denoising techniques to prevent this type of corruption from digital images. There are numerous techniques available for the purpose. There are two fundamental approaches to image denoising Spatial domain filtering, Transform domain filtering. These approaches are further divided into linear, non linear, FFT, UDWT etc. The future scope after reading these technique is to Denoising image by treated the image's pixels and its neighbors as vector variables whose training samples are selected from local windows using block matching based LPG. This ensures only the similar samples are selected for the PCA transformation so that the desired local characteristics are only preserved with considerable noise reduction. The LPG-PCA algorithm is performed twice to enhance the quality of an image.