Digital image processing is the manipulation of images using specific algorithms for the purpose of feature extraction. The filters applied are heavily dependent on the domain used. The spatial domain uses images just as they are and applies the required filter through a process called covolution. However for the frequency domain, the image is first required to be converted into to its frequency distribution using a process called transformation.
The list of filters enabled in this project are given below. Usage of this implementation is domain specific and thus, their respective attributes must be specified to get the correct output.
- Laplacian Filter
- Sobel Filter
- Prewitt Filter
- Statistical Filters
- Ideal Filter
- Gaussian Filter
- Butterworth Filter
Input - Image Format
python main.py --p \dip-cli\samples\sample.png --dim 300x300 --s laplacian
Input - Matrix in a text format
python main.py --p \dip-cli\samples\test.txt --dim 3x3 --f gaussian --d 10 --n 1.2
Path - To take image or text file containing the matrix as input
Dimension - To specify the size of the output
Domain - Spatial\Frequency Domain
Filter - Specified Filter
D0\N - Threshold Values
Save - Save\Display Output
