Research of improved non-local mean filtering algorithm of infrared images
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Department of Information Management, Henan Vocational College of Economics and Trade, Zhengzhou 450018, China
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Received Date:
2014-07-19
Accepted Date:
2014-10-15
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Abstract
In order to filter the noise in infrared images effectively, an improved non-local mean filtering (INLMF) algorithm was proposed. In the traditional non-local means filtering (NLMF) algorithm, the square image blocks of fixed size cannot depict the image details effectively. For overcoming the defects of NLMF, a novel adaptive classification method of image blocks, combing with gray scale information of image pixels, was put forward. The divided image block in size and shape depended on the actual distribution of gray scale information. And then, structure similarity (SSIM) factor was introduced to improve the calculation method of image blocks weights. Two infrared monitoring images were filtered by two traditional NLMF algorithms and the new INLMF algorithm. The theoretical and experimental results show that the performance of INLMF is superior to the others. It is helpful for enhancing the filtering effects of infrared images.
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