Image Denoising Algorithm Based on Improved Wavelet Threshold Function and Median Filter

Abstract

An improved wavelet threshold function is designed in this paper. The wavelet decomposing detail coefficients of the image mixed with Gaussian noise are denoised by this improved threshold function, then reconstructed together with the wavelet decomposing approximation coefficients to get the denoised image. The experimental results show that the denoising effect of the improved threshold function is superior to hard threshold and soft threshold. Different thresholds will be set for wavelet details of each level when denoising. Considering the advantage of median filter, this paper proposes to combine wavelet improved threshold denoising with median filtering, namely combined denoising method. MATLAB simulation results show that, this method has better effect on removing Gaussian noise, speckle noise and salt- and-pepper noise mixed in images. This paper also does the denoising experiments on fingerprint images and printed circuit board images contaminated by various noises, and the better denoising effect demonstrates the adaptability of the combined denoising method.