Improvement Of Quantized Adaptive Switching Median Filter For Impulse Noise Reduction In Grayscale Digital Image
In this dissertation, an improvement to Quantized Adaptive Switching Median filter (QSAM) has been done, to make it more efficient in reducing high density fixedvalued impulse noise from grayscale digital images. QSAM uses the switching approach, where it has noise detection and noise cancellatio...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2017
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| Subjects: | |
| Online Access: | http://eprints.usm.my/37339/1/AHMED_KHALDOON_ABDALAMEER_24_Pages.pdf |
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| Summary: | In this dissertation, an improvement to Quantized Adaptive Switching Median
filter (QSAM) has been done, to make it more efficient in reducing high density fixedvalued
impulse noise from grayscale digital images. QSAM uses the switching
approach, where it has noise detection and noise cancellation blocks. This approach
minimizes unwanted changes from the filtering process. QSAM also uses adaptive
approach, where the filter size is adaptable to the local noise content. QSAM has two
main stages. In the first stage, the image is filtered using the filtering window with
quantized size. In the second stage, the image is filtered using adaptive window size.
Improvement to QSAM has been carried out by replacing the formula used to restore
the corrupted pixel. Instead of using the local median value, this proposed method
uses the average of the local mean and local median values. Experimental results using
three standard grayscale images of size 512 512 pixels show that the proposed
method has the ability to restore the corrupted images even up to 95% of corruption.
As compared to other thirteen median filters, the proposed method had the lowest
Mean Square Error (MSE) and produce outputs with the best visual appearance. |
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