Adaptive wavelet thresholding method for image denoising
Yutao Ma · Guangdian gongcheng · 2007
An improved adaptive wavelet thresholding method for image denoising was proposed to overcome the limitation of Donoho's VisuShrink and Lakhwinder Kaur's NormalShrink. According to the different sub-band characteristics, a new scale parameter equation was defined based on Lakhwinder Kaur's NormalShrink threshold, which was employed to determine the optimal thresholds for each step scale. Experimental results on several testing images show that the proposed method separates signals from noise completely in each step scale and eliminates white Gaussian noise effectively. In addition, the method also preserves the detailed information of the original image well, obtain superior quality image and improves Peak Signal to Noise Ratio (PSNR). Furthermore, since this method can improve the efficiency of image denoising and doesn't increase time complexity, it could be applied in the real-time processing.