Weight Median Filter Using Neural Network for Reducing Impulse Noise
Noise is undesired information that affects an image. Noise appears in images from various sources. Noise reduction and noise removal is an important task in images processing. The weight median filters are extension of the median filter; it belongs to the broad class of nonlinear filters. Weigh...
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| Main Author: | |
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| Format: | Thesis |
| Language: | English English |
| Published: |
2004
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| Subjects: | |
| Online Access: | http://psasir.upm.edu.my/id/eprint/69/1/1000548974_t_FK_2004_6.pdf |
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| Summary: | Noise is undesired information that affects an image. Noise appears in images from
various sources. Noise reduction and noise removal is an important task in images
processing.
The weight median filters are extension of the median filter; it belongs to the broad class
of nonlinear filters. Weight median filter is more effective form of image processing, it
is the removing ability of impulsive noise. Impulsive noise is a kind of image corruption
where each pixel value is replaced with an extremely large or small value that is not
related to the surrounding pixel values by a probability.
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The design of weight coefficients of the weight median filter is considered as a difficult
problem. The weight coefficients of the weight median filter learnt by the backpropagation
with supervised multi-layer perceptron feed-forward networks and threshold
decomposition has been presented in this thesis, which has been implemented using
Turbo C++ language.
Good results have been achieved by using program package. Results show that weight
median filter based on threshold decomposition removes impulsive noise with an
excellent image detail-preserving capability compared to nonlinear filter and linear filter.
Restored images evaluation by using mean square error and speed. The package has
been implemented using the MATLAB language.
This study provides three types of filtering windows size, 3×3, 5×5 and 7×7 window
size. The result shows that the mean square error of weight median filter based on
threshold decomposition using 3×3 filtering window is less than 5×5, and 7×7 filtering
window and the speed of weight median filter based on threshold decomposition using
3×3 is faster than 5×5, and 7×7 filtering window. |
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