Image denoising using redundant finer directional wavelet transform

Abstract

In this paper, we propose two designs of redundant finer directional wavelet transform (FiDWT) and explain its application to image denoising. 2-channel perfect reconstruction (PR) checkerboard-shaped filter bank (CSFB) is at the core of the designs. The 2-channel CSFB, uses 2-D nonseparable analysis and synthesis filter responses without downsampling/upsampling matrices resulting in redundancy factor of 2. Both these designs have two lowpass and six highpass directional subbands. The hard-thresholding results for image denoising using proposed designs clearly shows improvement in PSNR as well as visual quality of the denoised images. Using the Bayes least squares-Gaussian scale mixture (BLS-GSM), a current state-of-the-art wavelet-based image denoising technique with the proposed two times redundant FiDWT design indicates encouraging results on textural images with much less computational cost.