A Image Denoising Improved Method Between Soft and Hard Adaptive Thresholding Based on Curvelet Transform
Yawen Wang · Aeronautical Computing Technique · 2009
Curvelet transform is a new kind of multiscale analysis technique after wavelet transform and ridgelet transform,which is able torepresent smooth and edge parts of image with sparsity.In addition,the representation contains more directional.According to the defects of soft thresholding and hard thresholding image denoising methods,the method between soft and hard thresholding image denoising approach in curvelet domain is proposed,and the curvelet transform coeffi-cients in different subbands are filtered with adaptive thresholds.Experiment results show that the new method has the advantages in denoised images with higher quality recovery of edges and curvilinear features.It is capable of achieving the higher peak signal-to-noise ratio(PSNR) and giving better visual quality.