Abstract
In this paper, a novel algorithm called a Nonlocal Adaptive Mean Filter (NAMF) for removing salt-and-pepper (SAP) noise from corrupted images is presented. We employ an efficient window detector with adaptive size to detect the noise. The noisy pixel is then replaced by the combination of its neighboring pixels, and finally, a SAP noise based nonlocal mean filter is used to reconstruct the intensity values of noisy pixels. Extensive experimental results demonstrate that NAMF can obtain better performance in terms of quality for restoring images at all levels of SAP noise.
Highlights
Digital images are often corrupted by noises in the process of image acquisition and transmission [1, 2]
We use 16 typical images and 40 test images collected from the TEST IMAGES Database [19] to evaluate our method. e experimental results demonstrate that Nonlocal Adaptive Mean Filter (NAMF) outperforms the existing state-of-the-art methods at both high SAP noise levels and low SAP noise levels
NAMF are compared with 8 state-ofthe-art methods: adaptive median filter (AMF) [9], noise adaptive fuzzy switching median filter (NAFSMF) [12], Adaptive weighted mean filter (AWMF) [13], the method proposed in [14], based on pixel density filter (BPDF) [15], Different applied median filter (DAMF) [16], AFWM [17], and adaptive switching weight mean filter (ASWMF) [18]
Summary
Digital images are often corrupted by noises in the process of image acquisition and transmission [1, 2]. Based on AMF, noise adaptive fuzzy switching median filter (NAFSMF) recognizes SAP noise by analyzing the histogram of noisy images and takes a fuzzy method to denoise [12]. Mathematical Problems in Engineering e processes of the above denoising methods can be divided into two stages, the detection of noisy pixels and the restoration of noisy pixels. For the latter one, the restoration of SAP noise is using the rest information (uncontaminated pixels) to repair the absent information (contaminated pixels). To solve the above problem, in this paper, we propose a nonlocal adaptive mean filter (NAMF) to remove SAP noise. We use 16 typical images and 40 test images collected from the TEST IMAGES Database [19] to evaluate our method. e experimental results demonstrate that NAMF outperforms the existing state-of-the-art methods at both high SAP noise levels and low SAP noise levels
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