Directional image denoising method based on the relative intersection of confidence intervals rule

Abstract

In this paper, the relative intersection of confidence intervals (ICI) rule is used to adaptively determine window sizes around each observed point in purpose of denoising. The relative ICI rule defines neighbourhoods of similar statistical properties for every signal sample. If we calculate a mean value on each window, it corresponds to the zero-order estimation and results in a denoised signal. Furthermore, the mean value can be replaced by median for additional robustness of estimation. The same approach could be taken on images. In this paper, we find the maximum window length in four, eight or sixteen directions around each pixel. Mean or median value of chosen surrounding pixels results in a denoised estimation of each observed pixel. The proposed denoising method was tested on an example of a piecewise constant image and compared to known methods. Under the given conditions, it has shown improvement in terms of the PSNR, MAE and subjective visual impression.