Image noise quality assessment using wavelet transforms

Image noise quality assessment is a major issue found in various image processing and computer vision problems. It is always a challenge to preserve important features, such as corners, edges and other sharp structures, during the denoising process and wavelet analysis has been demonstrated to be a powerful method for performing image noise reduction. Wavelet transforms have been widely used for image denoising since they provide a suitable basis for separating noisy signal from the image signal. This paper describes a powerful image denoising method based on wavelet transforms. The decomposition is performed by dividing the image into a set of blocks and transforming the data into the wavelet domain. The estimation of the standard deviation of noise contaminating an image is a fundamental step in wavelet-based noise reduction techniques. Experimental results, compared to different approaches, it has been revealed that the proposed method is suitable for different classes of images contaminated by Gaussian noise.