Local Adaptive Wavelet Threshold Denoising Based on Elliptic Directional Window and Edge Detection

EasyChair Preprint 6136

Abstract

Due to the sampling method for the wavelet coefficients of image can better adapting to its main directional characteristics, and the edge detection protects the edge information of the image, a local adaptive wavelet denoising method based on elliptic direction window and edge detection is proposed in this paper. The method first performs wavelet decomposition for image, and performs edge detection on the wavelet coefficients. Then, the wavelet coefficients of image are sampled by the elliptic directional window, and the local threshold of it is calculated. Next, the wavelet coefficients are quantized by soft threshold function. Finally, the denoised image is obtained by inverse wavelet transformation. In addition to be noted that a weight less than 1 is multiplied to reduce the threshold amplitude as much as possible to preserve the edge features of the image. In order to validate the performance of the proposed denoising method, four standard gray-scale test images are employed and the denoising results are compared with the Local Wiener Filtering with Directional Windows (LWFDW). The experimental results show that the method proposed in this paper performs better in terms of numerical indicators, is smoother in visual and has fewer pseudo-Gibbs phenomena than the LWFDW.

Keyphrases: Denosing, edge detection, elliptic directional window, local threshold, wavelet transform

@booklet{EasyChair:6136,
  author    = {Ningxin Fan and Songlin Zhang and Yali Li and Jie Han},
  title     = {Local Adaptive Wavelet Threshold Denoising Based on Elliptic Directional Window and Edge Detection},
  howpublished = {EasyChair Preprint 6136},
  year      = {EasyChair, 2021}}