Image denoising by using nonseparable wavelet filters and two-dimensional principal component analysis

In this paper, we propose an image denoising method based on nonseparable wavelet filter banks and two-dimensional principal component analysis (2D-PCA). Conventional wavelet domain processing techniques are based on modifying the coefficients of separable wavelet transform of an image. In general, separable wavelet filters have limited capability of capturing the directional information. In contrast, nonseparable wavelet filters contain the basis elements oriented at a variety of directions and different filter banks capture the different directional features of an image. Furthermore, we identify the patterns from the noisy image by using the 2D-PCA. In comparison to the prevalent denoising algorithms, our proposed algorithm features no complex preprocessing. Furthermore, we can adjust the wavelet coefficients by a threshold according to the denoising results. We apply our proposed technique to some benchmark images with white noise. Experimental results show that our new technique achieves both good visual quality and a high peak signal-to-noise ratio for the denoised images.