Image De-noising through Symmetric, Bell-shaped, and Centered Weighted Median Filters Based Subband Decomposition

Here, a novel image denoising algorithm that eliminates different type of noise from an image using median filter based subband decomposition. The benefit of sub-band decomposition using median transform over the wavelet decomposition method is that the nonlinear filters are not subject to Gibb's phenomenon which causes the ringing effects associated with the linear subband methods and they can be computed with low computational complexity. It has been experimentally observed that noisy coefficients have a higher value at the first scale of multiresolution analysis and later the value decreases in the subsequent scale and so on. Hence, we proposed to apply subsequently decreasing threshold on every single step of the multiresolution coefficients such that the de-noising method filters out the noise while preserving good image quality. A number of noisy images contaminated with different combinations of the Gaussian, Speckle and Salt and pepper noises are denoised by this new approach and compared using SNR measure with other wavelet denoising algorithms. The experimental results validate that the proposed algorithm outperforms the traditional wavelet decomposition method for noise removal.