Nonlocal Means Based Image Filtering Using Similarity Validation

Nonlocal means is one of the well-known and mostly used image denoising methods. The conventional nonlocal means approach uses weighted version of all patches in a search neighborhood to denoise the center patch. However, this search neighborhood can include some dissimilar patches. In this paper, we propose a pre-processing hard thresholding algorithm that eliminates those dissimilar patches. Consequently, the method improves the performance of nonlocal means. The threshold is calculated based on the distribution of distances of noisy similar patches. The method denoted by Similarity Validation Based Nonlocal Means (NLM-SVB) shows improvement in terms of PSNR and SSIM of the retrieved image in comparison with nonlocal means and some recent variations of nonlocal means. INTRODUCTION As digital imaging technologies become more advanced, the issue of image denoising still remains as a challenging stage. Removing additive noise is an essential pre-processing step in the majority of image processing techniques such as classification and object recognition, or it can be used for the purpose of improving image visual quality. Some of the earliest methods of denoising are simple averaging filters such as mean, median, Gaussian smoothing filters, and bilateral filters [1]. There are methods that transform data to other bases for the purpose of denoising such as wavelet or curvelet based methods [2]. The concentration of this paper is on nonlocal means methods (NLM) that are preferred when algorithm complexity is an issue. Most local methods only consider a local patch around the target pixel, assuming adjacent pixels tend to have similar patches. On the other hand, nonlocal means takes advantage of existence of a pattern or similar features in including the non-adjacent pixels [3]. NLM exploits selfsimilarities in the search neighborhood to estimate the true value of the noisy pixel. Since the introduction of NLM, many other variations have been proposed to further improve the method from various perspectives. For example, NLM with shape adaptive patches (NLM-SAP) is examined in [4]. The work in [5], improves NLM by a post processing denoising step based on method noise smoothing. Another recent improvement, probabilistic nonlocal means (PNLM) [6], implements a new weight function based on the distribution of the distances of similar patches. This weighting scheme outperforms the Gaussian kernel weights in traditional NLM. Regardless of the choice of the weights, many dissimilar patches in the search neighborhood are processed through NLM. Methods such as probabilistic early termination (NLM-PET) [7] attempt to reduce this number by a pre-processing hard-thresholding. However, the overall performance of this method is worse than that of the traditional NLM. A pre-filtering process is suggested in [8] to eliminate unnecessary patches by comparing gradient and average gray value of candidate similar patches. Motivated by the issue of unnecessary processing of dissimilar patches, we propose a new hard thresholding pre-processing algorithm to eliminate dissimilar patches before the weighting process. Our proposed method is faithful to the probabilistic distribution of the distance of similar patches. Our simulation results confirm superiority of this approach compared to the traditional NLM and the above variations of this method. PROPOSED METHOD: Our proposed method, denoted by similarity validation based nonlocal means (NLM-SVB), consists of three steps shown in Figure 1. In the following these three steps are explained in detail.