Segmentation-based document image denoising

In this work, a robust method of document images denoising is presented. The simple idea is combining the NLM filter and a Markovian segmentation into regions. The NLM method filtering allows participation of far, but proper pixels in the denoising process. Although the weights of non-similar (irrelevant) pixels are very small, high number of these pixels results in introduction of blur. In this work we present a new method to select the best candidate pixels based on their similarity. Before performing denoising process, we segment the noisy image into regions where similar pixels belong to a same homogeneous region r. Thus, to denoise a given pixel i, which belong to a region r_i, the proposed algorithm looks for the neighbor pixels of i and includes only those belonging to same region r_i. This method is tested on real noisy document images with promising results and it presents an improvement comparing to the original NLM.