Submission declined on 10 June 2024 by MicrobiologyMarcus (talk).
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Submission declined on 10 June 2024 by DoubleGrazing (talk). This submission appears to be taken from http://www.ijstm.com/images/short_pdf/1452346135_P48-55.pdf. Wikipedia cannot accept material copied from elsewhere, unless it explicitly and verifiably has been released to the world under a suitably free and compatible copyright license or into the public domain and is written in an acceptable tone—this includes material that you own the copyright to. You should attribute the content of a draft to outside sources, using citations, but copying and pasting or closely paraphrasing sources is not acceptable. The entire draft should be written using your own words and structure. Declined by DoubleGrazing 4 months ago.This submission has now been cleaned of the above-noted copyright violation and its history redacted by an administrator to remove the infringement. If re-submitted (and subsequent additions do not reintroduce copyright problems), the content may be assessed on other grounds. |
The alpha-trimmed filter is a statistical tool used in image processing for noise reduction.:[1] It combines elements of both median and mean filters, leveraging their advantages. The filter operates by discarding a specified number of the smallest and largest values in a dataset, guided by a trimming parameter alpha (α) which ranges from 0 to 0.5. The trimmed mean is then calculated from the remaining values, striking a balance between the median and moving average filters. This approach is particularly effective at reducing noise while maintaining the integrity of the original signal, making it a valuable technique for image enhancement and noise reduction. This is an algorithmic approach that tries to combine properties of the mean filter with properties of the median filter.
Removal of salt and pepper noise
editThe proposed system involves developing an adaptive alpha-trimmed median filter designed to eliminate salt and pepper noise from digital images. This filter selectively adjusts corrupted pixel values, effectively restoring images while preserving edge details. When tested across different noise levels, the filter consistently shows enhanced performance in terms of PSNR and IEF, highlighting its significant contribution to digital image processing.
Code
editThe alpha-trimmed mean filter algorithm takes the general formula[2]
- Select a suitable value of alpha
- Arrange all the image pixel values in increasing or decreasing order
- Trim alpha elements from the start and end
- Take average of the remaining pixel values
The entire algorithm as function:
void calculateTrimmedMean(const element* signal, int N, int alpha) {
if (2 * alpha >= N) {
std::cerr << "Invalid alpha value: The array is too small to trim " << alpha << " elements from both ends." << std::endl;
return;
}
element sum = 0;
int count = 0;
std::cout << "Remaining elements after trimming: ";
for (int i = alpha; i < N - alpha; ++i) {
std::cout << signal[i] << " ";
sum += signal[i];
++count;
}
std::cout << std::endl;
float mean = (float)sum / count;
std::cout << "Mean of the remaining elements: " << mean << std::endl;
}
Alpha (α)-Trimmed Averaging Filter Example
editWindow F
edit20 | 20 | 18 |
21 | 19 | 12 |
19 | 22 | 10 |
Ordered List
editThe ordered list is:
- F = { 10, 12, 18, 19, 19, 20, 20, 21, 22 }
Alpha-Trimmed Mean Calculation
editFor α = 1
editTrim 1 smallest and 1 largest elements. Remaining list:
- { 12, 18, 19, 19, 20, 20, 21 }
Mean:
- (12 + 18 + 19 + 19 + 20 + 20 + 21)/7 = 129/7= 18.43
For α = 2
editTrim 2 smallest and 2 largest elements. Remaining list:
- { 18, 19, 19, 20, 20 }
Mean:
- (18 + 19 + 19 + 20 + 20)/5 = 96/5 = 19.2
For α = 3
editTrim 3 smallest and 3 largest elements. Remaining list:
- { 19, 19, 20 }
Mean:
- (19 + 19 + 20)/3 =58/3=19.33
For α = 4
editTrim 4 smallest and 4 largest elements. Remaining list:
- { 19 }
Mean:
- 19
References
edit- ^ "Image Enhancement using α-Trimmed Mean εFilters". World Academy of Science, Engineering and Technology 59 2011.
- ^ Jayaraman, S.; Esakkirajan, S.; Veerakumar, T. (2009). Digital Image Processing. Tata McGraw Hill Education. ISBN 9780070144798.
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