A Quadrant Cluster for Outlyingness detail Detection to identify Noise

SEEE DIGIBOOK ON ENGINEERING & TECHNOLOGY, VOL. 01, FEB 2018 PP.(178-183)
Abstract– Outlyingness is a kind of noise, which is also known as impulse signal occurs at the unsuitable position. This outlyingness noise occurs when the sudden change in frequency happens. While removing these outlyingness noises or Gaussian mixed noise, the algorithm may tend to remove the sharpness of an image. This made to propose a replacement detection mechanism for almost any type of impulse noise, which works based on nonlocal means that (NL-means). The operation is dispensed in 2 stages, i.e., detection followed by filtering. For detection, we propose the Outlyingness Magnitude Relation (OMR) for measurement and to analyze the relation. previously the impulse-like actual pixel elements will be removed with the old traditional algorithms. This OMR measure and divides the difference between the actual pixel and the noise by generating clusters inside the image components. This clustering process continues as recursively with reference to the noise density available. This process will be handled within the divided quadrant and relative nature. OMR will cluster the image elements from-coarse-to-fine Outlyingness ratio is measured to identify the noise. The projected OMR-NLM additionally achieves high peak ratio and nice image quality by expeditiously removing impulse/Gaussian mixed noise. The main advantage of using this method is it can be easily implemented with VL.
Index Terms – Image denoising, outlyingness Ratio, Noise detection, nonlocal means, OMR.
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P.Gnanambikai1, Dr.K.N.Vijeyakumar2
1 Nachimuthu Polytechnic college,
Coimbatore, India
2 Dr.Mahalingam College of Engineering & Technology,
Coimbatore, India
pgnans22@gmail.com

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