Adaptive wavelet thresholding & joint bilateral filtering for image denoising

This paper proposes an efficient algorithm for removing noise from corrupted images by incorporating a wavelet based thresholding with a spatial based joint bilateral filter. Although wavelet-based methods are efficient in image denoising, they are prone to producing low-frequency noise and edge ringing which relate to the structure of the underlying wavelet. On the other hand, most spatial-based algorithms output much higher quality denoising image with less artifacts. However, they are usually too computationally demanding. In order to reduce the computational cost, an efficient joint bilateral filter by using the wavelet denoising result rather than directly processing the noisy image in the spatial domain is developed. The joint bilateral filtering is applied to the approximation (low-frequency) subband of wavelet decomposed image using a wavelet filter bank and wavelet thresholding method is applied to the detailed subbands. The proposed method for image denoising is demonstrated on a number of standard images and the performances are evaluated in terms of peak signal to noise ratio.