Image denoising using directional filter banks

The use of wavelet thresholding has been investigated with much success in the areas of denoising, density estimation, image restoration, etc. Significant attention has been given to wavelet thresholding as a signal denoising technique. The algorithm is simple and provides good results. The 2-D discrete wavelet transform (DWT) and its relatives have been used to generalize the denoising methods to images. However the DWT is limited in its representation of directional information like edges and some types of texture. We propose the use of a directional filter bank for image denoising under the same premise as wavelet thresholding: small magnitude subband coefficients represent noise and can be replaced with zeros while large coefficients reflect, in our case, strong signal content in a given direction. We show that the directional filter bank is capable of preserving edge information better than DWT based techniques while effectively removing noise. The proposed technique provides sharp images with higher perceptual quality.