A new image denoising method based on thewavelet domain nonlocal means filtering
We present a new image denoising method based on the nonlocal means filtering in the wavelet domain. A noisy image is first decomposed into subbands by wavelet transform and the nonlocal means filter is applied to each subband. It is also noted that the performance of the nonlocal means filter depends on the kernel bandwidth (size of the filter) and the image properties. Hence we propose a method to adjust the kernel bandwidth for each of the subband images, based on the estimation of noise statistics. This filtering method preserves the wavelet coefficients corresponding to the structures, while effectively suppressing noisy ones. Experimental results show that the proposed method provides comparable or sometimes higher peak signal-to-noise ratio (PSNR) than the state-of-the-art wavelet denoising methods and the spatial nonlocal means filter. Subjective comparison also shows that the proposed method provides better contrast than the spatial nonlocal means filter, and less ringing artifacts that commonly arise in the conventional wavelet denoising.