Edge-preserving Image Denoising Method Based on Dyadic Lifting Schemes

Abstract

In this paper, we propose a new wavelet denoising method with edge preservation for digital images. Traditionally, most denoising methods assume additive Gaussian white noise or statistical models; however, we do not make such an assumption here. Briefly, the proposed method consists of a combination of dyadic lifting schemes and edge-preserving wavelet thresholding. The dyadic lifting schemes have free parameters, enabling us to construct filters that preserve important image features. Our method involves learning such free parameters based on some training images with and without noise. The learnt wavelet filters preserve important features of the original training image while removing noise from noisy images. We describe how to determine these parameters and the edge-preserving denoising algorithm in detail. Numerical image denoising experiments demonstrate the high performance of our method.

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“…Formerly, many researchers had conducted image processing using Dyadic wavelet transformation. As in [4] and [5], they were applying this method for image denoising purpose. The results for both researchers were satisfying.…”

“…A Dyadic wavelet transform The downsampling wavelet, which samp les the scale and translation parameters, is often failing when deal with some assignments such as edge detection, features extraction, and image denoising [5]. Different with the downsampling wavelet, the dyadic wavelet samples only the scale parameter of a continuous wavelet transform, and does not samples the translation factor.…”

“…since �( 0 ) = 1 , we can apply (5) and (6) to obtain ℎ �( 0 ) = √2. Using the scaling function and the wavelet filter [ ] , we define a dyadic function by…”