8 March 2018 A fast non-local means algorithm based on integral image and reconstructed similar kernel
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Proceedings Volume 10609, MIPPR 2017: Pattern Recognition and Computer Vision; 106091L (2018) https://doi.org/10.1117/12.2288315
Event: Tenth International Symposium on Multispectral Image Processing and Pattern Recognition (MIPPR2017), 2017, Xiangyang, China
Abstract
Image denoising is one of the essential methods in digital image processing. The non-local means (NLM) denoising approach is a remarkable denoising technique. However, its time complexity of the computation is high. In this paper, we design a fast NLM algorithm based on integral image and reconstructed similar kernel. First, the integral image is introduced in the traditional NLM algorithm. In doing so, it reduces a great deal of repetitive operations in the parallel processing, which will greatly improves the running speed of the algorithm. Secondly, in order to amend the error of the integral image, we construct a similar window resembling the Gaussian kernel in the pyramidal stacking pattern. Finally, in order to eliminate the influence produced by replacing the Gaussian weighted Euclidean distance with Euclidean distance, we propose a scheme to construct a similar kernel with a size of 3 x 3 in a neighborhood window which will reduce the effect of noise on a single pixel. Experimental results demonstrate that the proposed algorithm is about seventeen times faster than the traditional NLM algorithm, yet produce comparable results in terms of Peak Signal-to- Noise Ratio (the PSNR increased 2.9% in average) and perceptual image quality.
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Zheng Lin, Zheng Lin, Enmin Song, Enmin Song, } "A fast non-local means algorithm based on integral image and reconstructed similar kernel", Proc. SPIE 10609, MIPPR 2017: Pattern Recognition and Computer Vision, 106091L (8 March 2018); doi: 10.1117/12.2288315; https://doi.org/10.1117/12.2288315
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