An adaptive edge-preserving image denoising technique using tetrolet transforms

Denoising has always been one of the standard problems in image processing. It is always desirable to preserve important features, such as edges, corners and other sharp structures, during denoising. Wavelet transforms have been widely used in edge-preserving image denoising since these provide a suitable basis for suppressing noisy signals from the image. This paper presents a novel edge-preserving image denoising technique based on tetrolet transform (a Haar-type wavelet transform) and a locally adaptive thresholding method. The noisy image is decomposed into tetrolet (wavelets) coefficients through a tetrolet transform. A locally adaptive thresholding method exploiting interscale statistical dependency and based on computation of noise level is used to threshold the tetrolet coefficients to effectively reduce noise while preserving relevant features of the original image. Experimental results, compared to other approaches, demonstrate that the proposed method is suitable especially for the natural images contaminated by Gaussian noise.