Image Processing in Matlab

Basic concepts of Image processing with examples in Matlab

Content

1. Intensity Transformations
2. Image Filtering in the Spatial Domain
3. Edge detection
4. Image Filtering in frequency domain
5. Image restoration
6. Image segmenation
7. Morphology
8. Regionprops

1. Intensity Transformations

1. histogram.m - Generatate histogram for grayscale image
2. histogram_color_image.m - Generate histogram for color image
3. rgb_histogram.m - Function for generating color image histogram
4. pixel_value_filter.m - Filtering image by pixel value
5. filter_by_value.m - Detecting background by color
6. pixel_transformation.m - Function for chaging pixel value based on Look-up table (LUT)
7. pixel_lut_exp - Exponentional function aproximated with LUT
8. pixel_lut_log - Log function aproximated with LUT
9. pixel_lut_gama - Power function aproximated with LUT
10. histogram_equalization - Histogram equalization
11. adaptive_histogram - Adaptive histogram equalization
12. rgb_histogram_euqalization.m - Histogram equalization on color images

2. Image Filtering in the Spatial Domain

Examples:

1. noise.m - Add noise to the image
2. mean_filter.m - Mean filter in spatial domain
3. median_filter.m - Median filter in spatial domain
4. order_statistic_filtering.m - Order-statistic filtering in spatial domain
5. gaussian_filter.m - Gaussian filtering in spatial domain

3. Edge detection

Examples

1. first_order_edge_detection.m - Edge detection using kernel aproximation of first order deviation
2. second_odred_edge_detection.m - Edge detection using kernel aproximation of second order deviation
3. zero_crossing_edge_detector.m - LoG filter
4. edge_emphasise.m - Emphasasing edges using LoG filter

4. Image Filtering in frequency domain

TO implement image filtering in frequnecy domain it is required to follow steps listed below:

1. Read image (imread());
2. Obtain the Fourier transform F of the image
3. Generate the filter function H, the same size as the image
4. Multiply the transformer image by the filter G = H .* F
5. Optain inverse FFT of the G
6. Scale the output image

Examples

1. fft_image.m - Generate fft for an image.
2. ideal_lp_filter.m - Ideal low pass filter
3. ideal_hp_filter.m - Ideal high pass filter
4. butterworth_lp_filter.m - Butterworth low pass filter
5. butterworth_hp_filter.m - Butterworth high pass filter
6. gauss_lp_filter.m - Gauss low pass filter
7. gauss_hp_filter.m - Gauss high pass filter

5. Image restoration

Examples

1. periodic_noise.m - Add periodic noise to the image
2. filtering_periodic_noise.m - Remove periodic noise using band stop filter
3. inverse_filtering.m - Inverse filtering
4. inverse_filtering_constrained_division.m - Inverse filtering with constrained_division
5. inverse_filtering_lp_filter - Inverse filtering with low pass filter
6. Add motion debluring
7. Add Wiener filtering

6. Image segmenation

Examples:

1. threshold.m - Segmenation using manual threshold value.
2. histogram_approximation - Approximate histogram with polynomial function
3. otsu.m - Otsu's method
4. Add entropy classsification
5. Add isodata algorithm

7. Morphology

Examples

1. erosion.m - Erosion operation
2. dilatation.m - Dilatation operation
3. opening.m - Opening operation
4. closing.m - Closing operation
5. hit_or_miss_transformation.m - Not working like intended
6. opening_by_reconstruction.m

#8. Regionprops

Examples:

1. circle.m
2. bwlabel_example.m
3. boundingbox.m