Image Denoising Using Principal Component Analysis and Wavelet

This paper, proposes a method of removing noise from digital images corrupted with additive, multiplicative, and mixed noise using Principal Component Analysis in frequency domain. Principal component analysis (PCA) is a popular denoising technique used in spatial domain. The Wavelet transform has been widely used for image denoising due to its multi-resolution nature in frequency domain, its ability to produce high levels of noise reduction and the low level of distortion. We propose image denoising in wavelet domain using PCA. Each noisy image patch is then locally denoised within its neighborhoods using Principal component analysis. Modified coefficients are thresholded using wavelet transform, which results in good peak signal to noise ratio. Our proposed scheme applies at different noise density level in wavelet domain. This method preserves the edges and important information of image without blur.