Robust Bootstrap Confidence Intervals: An Application Study for Denoising Images

Blurred images are a common problem in image processing and image deblurring techniques are sensitive to image noise. Some recent proposals use confidence intervals to image deblurring under the usual assumptions of Gaussian noise. However, non-normal noise, and particularly the presence of outliers, severely degrades the performance of the restoration. This results in poor state estimates and invalid inference. In this work, we propose a new image cleaning method that removes noise in blurred images based on robust confidence intervals. We consider that the observation noise distribution can be represented as a member of a contaminated normal neighbourhood and the analysis is based on nonparametric bootstrap confidence intervals. An illustration of this technique is presented. From the results we conclude that, regardless the distribution of the random noise, we obtained a blurry image ready to start the restoration process, without the problem of random noise even though not normal distributed.