PSO-based parameters selection for the bilateral filter in image denoising
Chengyan Wang, Bing Xue, Lin Shang · Proceedings of the Genetic and Evolutionary Computation Conference · 2017
The bilateral filter method is a nonlinear filter with spatial averaging without smoothing edges. It has shown to be an effective image denoising technique. Denoising performance using the bilateral filter is affected by the filter parameters, which are image dependent and require experimental trials. We propose an automatic and effective PSO-based method of parameters selection for the bilateral filter in image denoising. Intensity domain parameter δr and the radius parameter d are optimized by the PSO algorithm, in which SSIM (structural similarity index) is employed in fitness function. We firstly compare our approach with other four classical filtering methods at different types and levels of noise. We also compare the denoising performance with different values of the parameter δd. Experimental results on three sets of color images have shown that the proposed method of parameter selection outperformed the other filtering methods in denoising standard test images corrupted by different types and levels of noise.