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6 July 2015 A new machine learning algorithm for removal of salt and pepper noise
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Proceedings Volume 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015); 96311R (2015) https://doi.org/10.1117/12.2197113
Event: Seventh International Conference on Digital Image Processing (ICDIP15), 2015, Los Angeles, United States
Abstract
Supervised machine learning algorithm has been extensively studied and applied to different fields of image processing in past decades. This paper proposes a new machine learning algorithm, called margin setting (MS), for restoring images that are corrupted by salt and pepper impulse noise. Margin setting generates decision surface to classify the noise pixels and non-noise pixels. After the noise pixels are detected, a modified ranked order mean (ROM) filter is used to replace the corrupted pixels for images reconstruction. Margin setting algorithm is tested with grayscale and color images for different noise densities. The experimental results are compared with those of the support vector machine (SVM) and standard median filter (SMF). The results show that margin setting outperforms these methods with higher Peak Signal-to-Noise Ratio (PSNR), lower mean square error (MSE), higher image enhancement factor (IEF) and higher Structural Similarity Index (SSIM).
© (2015) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Yi Wang, Reza Adhami, and Jian Fu "A new machine learning algorithm for removal of salt and pepper noise", Proc. SPIE 9631, Seventh International Conference on Digital Image Processing (ICDIP 2015), 96311R (6 July 2015); https://doi.org/10.1117/12.2197113
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