Using Complex Wavelet Transform and Bilateral Filtering for Image Denoising

TL;DR

This paper introduces a novel image denoising method combining complex wavelet transform and bilateral filtering, effectively reducing noise while preserving edges, demonstrated through experimental results on real data.

Contribution

The paper proposes a new denoising framework applying bilateral filtering to low-frequency subbands and thresholding to high-frequency subbands in the complex wavelet domain.

Findings

Effective noise elimination demonstrated on real data

Preserves edges while reducing noise

Outperforms traditional methods in experiments

Abstract

The bilateral filter is a useful nonlinear filter which without smoothing edges, it does spatial averaging. In the literature, the effectiveness of this method for image denoising is shown. In this paper, an extension of this method is proposed which is based on complex wavelet transform. In fact, the bilateral filtering is applied to the low-frequency (approximation) subbands of the decomposed image using complex wavelet transform, while the thresholding approach is applied to the high frequency subbands. Using the bilateral filter in the complex wavelet domain forms a new image denoising framework. Experimental results for real data are provided, by which one can see the effectiveness of the proposed method in eliminating noise.