Image Denoising using Common Vector Elimination by PCA and Wavelet Transform

This paper presents a novel image denoising technique by using Principal Component Analysis (PCA) and Wavelet transform. The noisy image can be decomposed by the PCA into different blocks. Eigen values for each block is calculated and the common vector from each block is eliminated. The noise under consideration is AWGN and is treated as a Gaussian random variable. The denoised image obtained by using the above algorithm is processed again by using wavelet transform. This post processing results in further improvement of the denoised results. Experimental results show better performance in terms of PSNR as compared to the performance of the methods when incorporated individually.