Images Restoration Based on the Textural Features

Image denoising is an important and widely studied problem in machine vision and image processing.However,a large number of image denoising methods eliminate noise and discard textures and edges,at the same time.To overcome the shortcomings,the paper makes its improvement on a basis of unsupervised,information-theoretic,adaptive image filter under analysis of difficulties on image denoising.According to the principles of signal energy,a detection operator of textural features is proposed to check the filter residue of the image in the time-domain and the frequency-domain respectively.Textures and details filtered out by mistake during the process of denoising will be extracted as much as possible.After the filtered image is compensated for missing information,the final denoised image is obtained.Experimental results show that the method can retain the image's textures and details,effectively eliminate image noise,increase the signal-to-noise ratio(SNR),reduce the mean square error,and significantly improve the image's visual effect.Thus,it is practicable.