Blind Noise Level Estimation and Blind Denoising Using Principal Component Analysis (International Journal of Engineering and Computer Science, 2014)
Noise is important factor in the image processing area .which is effect to visual quality of image and application of image processing like color information, restoration, and enhancement etc. In this paper consider blind noise which has no proper parameter for measure in practically .But this blind noise is already exist in image. In this method estimate to the blind noise from an image  using principal component analysis  with use parameters as weak texture with low rank texture strength .In this process estimate to the blind noise of an images of each  channel (Red ,Green, Blue )noise present in an  image. This estimated channel noise is blind noise .which again adding this channel noise in an image by using Bayer pattern. Then Blind noisy image have been denoising by using adaptive non local mean principal component analysis with color demosaicking and finally again estimate to the blind noise level of blind denoising image. Those Estimated noise levels have denoised an image. This is finally possibility of denoised of blind noisy image. The result is better visual quality and smooth sharp image.