Natural image denoising method based on nonnegative sparse coding shrinkage
De-Shuang Huang, Chunhou Zheng · 2006
Non-negative sparse coding(NNSC) relies on natural image statistical properties,therefore,it is self-adaptive.The feature basis vectors of natural images can be successfully extracted by NNSC.As a practical application of such basis vectors,a novel image denoising method based on NNSC shrinkage technique to reduce Gaussian additive noise was proposed.Experimental results show that the feature bases extracted are localized and oriented in the temporal and frequency domains,and this case shows efficiently edge features of natural images.Moreover,compared with the method of independent component analysis(ICA),the feature bases extracted by the NNSC algorithm exhibit much clearer edge features.In view of to the visual effect and the normalized signal-ratio-noise(NSNR) values of denoised images,the denoising results show that the NNSC shrinkage method outperforms any other types of denoising methods such as sparse coding(or ICA) shrinkage,wavelet-based soft shrinkage,Wiener filter,etc..