An Improved Image Denoising Algorithm based on Shearlet
Zhiyong Fan, Quansen Sun, Feng Ruan, Yiguang Gong, Zexuan Ji · 2013
In allusion to remove Racian noise while lessen the loss of details as low as possible, this paper proposed an filter algorithm which comprehensive utilize Multi-Objective Genetic Algorithm (MOGA) and Shearlet transform based on a Multi-scale Geometric Analysis (MGA) theory. First, it performs a wavelet multi-scale decomposition of image. Then, it builds target function in MOGA by several evaluation methods such as Signal to Noise Ratio (SNR). Third, it uses the MOGA to optimal coefficients of Shearlet wavelet threshold value in different scale and different orientation. Finally, it obtains the composite image by using inverse lifting wavelet transform. Experimental results show tha our proposed new algorithm presented here is more effective in removing Rician noise, and giving better Peak Signal Noise Ratio (PSNR) gains, without manual intervention in comparison with other traditional filters.