Abstract
The basic principle of nonlocal means is to denoise a pixel using the weighted average of the neighbourhood pixels, while the weight is decided by the similarity of these pixels. The key issue of the nonlocal means method is how to select similar patches and design the weight of them. There are two main contributions of this paper: The first contribution is that we use two images to denoise the pixel. These two noised images are with the same noise deviation. Instead of using only one image, we calculate the weight from two noised images. After the first denoising process, we get a pre-denoised image and a residual image. The second contribution is combining the nonlocal property between residual image and pre-denoised image. The improved nonlocal means method pays more attention on the similarity than the original one, which turns out to be very effective in eliminating gaussian noise. Experimental results with simulated data are provided.
Highlights
Image denoising [1,2,3,4,5,6,7,8,9,10] is a low-level image processing tool, but it’s an important preprocessing tool for high-level vision tasks such as object recognition [11,12], image segmentation and remote sensing imaging
Since the accuracy of similarity computation is clearly affected by how rich the priori information of noised images is, we hereby apply multiple noised images to improve the performance of nonlocal means (NLM) method
In the first step we utilize the nonlocal information in two noisy images to improve the accuracy of similarity calculation
Summary
Image denoising [1,2,3,4,5,6,7,8,9,10] is a low-level image processing tool, but it’s an important preprocessing tool for high-level vision tasks such as object recognition [11,12], image segmentation and remote sensing imaging. The definition of similarities between the patch of the noisy pixel and its spatially local neighborhood patches in NLM is not strict, it’s just calculated by a block matching process. The basic principle of the nonlocal means denoising is to replace the noisy gray-value I(i) of pixel i with a weighted average of the gray-values of all the pixels on the image. Because it needs too much computation, it is more practical to average the pixels in a smaller scope. In the first step we utilize the nonlocal information in two noisy images to improve the accuracy of similarity calculation. X where Z0i1 is the normalizing term, Z0i1 1⁄4 j1 o0i1j1 , With the new weight, we can calculate the estimated value of pixel i1 by
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