Combination of Spatial Filtering and Adaptive Wavelet Thresholding for Image Denoising

Thresholding in wavelet domain has proven very high performances in image denoising and particularly for homogeneous ones. Conversely, and in cases of relatively non-homogeneous scenes, it often induces the loss of some true coefficients; inducing so, to smoothing the details and the different features of the thresholded image. Therefore, and in order to overcome this shortcoming, we introduce within this paper a new alternative made by a combination of advantages of both spatial filtering and wavelet thresholding; that ensures well removing the noise effect while preserving the different features of the considered image. First, the degraded image is decomposed into wavelet coefficients via a 2-level 2D-DWT. Then, the finest detail sub-bands likely due to noise, are thresholded in order to maximally cancel the noise contribution. The remaining noise shared across the coarse detail subbands (LH2, HL2, and HH2) is cleaned by filtering these mentioned sub-bands via an adaptive wiener filter instead of thresholding them; avoiding so smoothing the acquired image. Finally, a joint bilateral filter (JBF) is applied to ensure the preservation of the different image features. Experimental results show notable performances of our new proposed scheme compared to the recent state-of-the-art schemes visually and in terms of (MSE), (PSNR) and correlation coefficient.