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Noise adding methods (impulse and Gaussian) are using in thiswork, adding this type of noise employ to mensuration the image quality asresult, therefore transform of (RGB) colors to another type of colorimpersonation that easy to deal with it, also sensitivity of human eyes to detectthe colors. Applying Noise reduction (mean method, mode method, medianmethod) ,By using a test image, we demonstrate that the filtering structureyields an output image which is significantly better than those of median,weighted median .The program of this work is written with Delphi Language,which have some flexibility to deal with image that's not found in otherlanguage. The great feature of this language and the ability to process theimage in memory (so the processing will be faster and easier), easy to accessthe parameters through the subroutines (however the size of it), showing thestored images that in memory to the displayer through one instruction.
Noise Reduction --- Gaussian Noise --- Signal to Noise Raito --- Noise adding Algorithms --- noisy image
A comparative study was conducted in this paper , between three algorithms which (Harris , Shi-Tomasi , FAST ) interested-points detection to identified the features that required to match , recognize and track objects in images noisy . Detect the interested-points in image noisy one of the most challenges in field of image processing . The noise consider the main cause for damage the natural images during the acquisition and transition , and detect the interested-points of these images doesn’t give the desired results , so eliminating noise from this images is very important , Non-local means approach is applied for solve this problem .
Harris Detector --- Shi-Tomasi Detector --- FAST Detector --- Noisy Image --- Non-Local Mean Filter.
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