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Adaptive Median Filter Based Noise Removal Algorithm for Big Image Data

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Document pages: 6 pages

Abstract: In this work, develop and evaluate a novel Adaptive Median Filter (AMF) for noise removal which is devoted in the direction of contribution a consistent and proficient model for increasing the image quality in the existence of high noise levels. AMF algorithm is proposed for noise removal in the direction of increases quality of the image by reducing noises in the images. Filtering methods such as Decision Based Filtering (DBF), Median Filter (MF) and AMF are evaluated in terms of their force on the following image analysis. The experimental results show with the purpose of all of these filtering methods is able to give better results and higher value as the amount of noise being removed increases. The filtering methods results are measured in terms of metrics like Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE), and time efficiency. The results analysis demonstrates that the proposed AMF algorithm is reliable for image data sets.

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