Removing as much noise as possible in an image while preserving its fine details is a complex and challenging task. We propose a wavelet-based and non-local means (NLM) denoising method to overcome the problem. Two well-known wavelets: dual-tree complex wavelet transform (DT-CWT) and discrete wavelet transform (DWT), have been used to change the noise image into several wavelet coefficients sequentially. NLM filtering and universal hard thresholding with cycle spinning have been used for thresholding on its approximation and detail coefficients, respectively. The inverse two-dimensional DWT was applied to the modified wavelet coefficients to obtain the denoised image. We conducted experiments with twelve test images on the set12 data set, adding the additive Gaussian white noise with variances of 10 to 90 in increments of 10. Three evaluation metrics, such as peak signal noise to rate (PSNR), structural similarity index metric (SSIM), and mean square error (MSE), have been used to evaluate the effectiveness of the proposed denoising method. From these measurement results, the proposed denoising method outperforms DT-CWT, DWT, and NLM almost in all noise levels except for the noise level of 10. At that noise level, the proposed denoising method is lower than NLM but better than DT-CWT and DWT.

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