1. Advanced Image Processing Lab MSc Project

2. Project description The Advanced Image processing Lab project is a GUI interface encapsulates all the lab experiment, algorithms and theoretical background of the Advanced Image processing Lab Course. Algorithms and UI are based on Matlab and Matlab Image processing Tool box GUI was built using Matlab “guide” tool for UI interface construction Based on Prof. Yaroslavsky’s book “Theoretical Foundations of Digital Imaging Using MATLAB”.

3. Purpose Easy and interactive experiments execution Single interface which provide a gateway to all of the Lab related materials To link between theory and practice To constitute an easy comparison tool of different image processing algorithms To constitute fast parameter effect on an algorithm evaluation platform To be the basis of a more in depth learning of advanced image processing lab topics

4. Project Structure 12 folders (one for each topic) - contains the lab experiments ‘htm’ folder– contains the lab documentation and theoretical background ‘Data’ folder – contains input images and signals ‘utils’ folder – contains common scripts and database files Two main files: AdvancedImageProcessingLab_mb.fig – Matlab figure which defines the UI AdvancedImageProcessingLab_mb.m – Matlab function with main and callback functions

5. GUI description There are two variations of the GUI: main topic navigation window – created by the ‘guide’ tool, and constitute as the basis to all of the experiment window UIs Experiment window – each experiment function defines a unique experiment window

6. Main topic navigation window The main topic navigation window is comprised of a welcome image, 12 pushbuttons, and a toolbar

7. Toolbar The tool bar contains tool which helps the user to inquire and investigate each experiment results. The toolbar consists of 9 buttons: Data cursor – enables inquiries of pixel or plot values at a certain positions. Load image\ load auxiliary image \ load 2nd auxiliary image – allows the user to change the input signal\image used by current experiment. For experiments with more than one input, load auxiliary image \ load 2nd auxiliary image buttons should be used. All experiments have default input image \signal, however it is possible to use user’s images \ signals. All image formats supported by matlab “imread” function are supported - ( 'bmp', 'cur’, ‘fts’, ‘fits’, ‘gif’, ‘hdf’, ‘ico’, ‘j2c’, ‘j2k’, ‘jp2’, ‘jpf’, ‘jpx’, ‘jpg’, ‘jpeg’, ‘pbm’, ‘pcx', 'pgm’, ‘png’, ‘pnm’, ‘ppm’, ‘ras’, ‘tif’, ‘tiff’, ‘xwd’), and raw ‘mat’ gray scale image files, for which the user will be prompt to provide the image size. It is possible to load an image of any size, but each image is resized to the size of 256x256. If the image is a color image, it will transform to gray. If the aspect ratio is not 1x1, the center of the image is cropped, to avoid distorting the image. Zoom in\out - allows the user to inspect image\signal by zooming in or out. Pan – allows the user to shift the image/signal right, left up or down. Help – upon pressing this button while in experiment, a detailed help document will be opened, with experiment tasks, questions, theoretical background and algorithm descriptions. Upon pressing this button while in main window, this chapter will appear. Interactive mode – This button controls the behavior of the program, when user changes the experiment setting. If it is set to ‘off’, then only once the user presses “run experiment’ will the experiment will be executed. If it is set to ‘on’, then whenever the user changes the experiment settings, immediately, the experiment is executed.

8. Topics list The twelve topics are: Image discretization Image quantization Image compression: predictive coding Image compression: transform coding Image statistics Image noise models Pattern formation models Image resampling Target location Image perfection: linear filters Image perfection: rank filters Image enhancement

9. GUI description: experiment window The experiment window is experiment result display, control panel, experiment selector, toolbar, and a ‘Back’ button

10. Matlab GUIDE tool The project was built using Matlab built-in tool ‘GUIDE’, the Open GUI Layout Editor tool for gui infrastructures Matlabs ‘GUIDE’ stores the GUI in two files: A FIG-file, with extension.fig, that contains a complete description of the GUI layout and each UI component, such as push buttons, axes, panels, menus, and so on. The FIG-file is a binary file and which cannot be modified except by changing the layout in GUIDE. FIG-files are specializations of MAT-files. A code file, with extension.m, that initially contains initialization code and templates for some callbacks that control GUI behavior. The code file contains the call back of the main FIG file UI components.

11. Matlab GUIDE tool

12. Main code file The code file structure is built of: Comments - Displayed at the command line in response to the help command. Initialization - GUIDE initialization tasks. Needed for proper operation of the code. Opening function - Performs your initialization tasks before the GUI user has access to the GUI. Output function - Returns outputs to the MATLAB command line after the opening function returns control and before control returns to the command line. Component and figure callbacks - Control the behavior of the GUI figure and of individual components. MATLAB software calls a callback in response to a particular event for a component or for the figure itself. Utility/helper functions - Perform miscellaneous functions not directly associated with an event for the figure or a component.

13. The guidata function and handles struct The guidata Matlab function store or retrieve GUI data - the ‘handles’ struct The ‘handles’ struct contains all the handles of the UI as well as other program parameters. All communication and data sharing between the different components are done with the ‘guidata’ function

14. Example top program flow Program checks validity of key folders, loads data base and upload the interface User run the AdvancedImageProcessingLab_mb function Program change current directory to the topics folder, execute the first default experiment and removes main window UI and image User select a topic by pressing the corresponding button Experiment program loads default UI, image\signal and algorithms parameters a displays the control panel and (mostly) test image Remove experiment UI and loads main window UI User pressed the ‘Back’ button

15. Example topic program flow Using the radio button, user change the current experiment Using change experiment parameters using the control panel Update the changed parameter, save current experiment status in ‘handles’ Experiment algorithms executed, results are displayed User press ‘Run experiment’

16. Experiment functions A standard experiment function file structure consists of: Comments Main function – defines the look of the experiment window, have similar name as the experiment Callbacks functions – for different UI tasks A “process_image” function to execute the algorithms and display the results Supplementary functions – for various tasks such as minimization of error etc

17. Common functions used DeployAxes - calculates the location of each axes position, and create those axes. SetSliderParams - defines a struct which describes a slider for a parameter. This struct is the passed to the SetInteractiveInterface function, which sets the sliders and other UIs. SetInteractiveInterface - This function creates the control panel, and all the UI (sliders, pushbuttons, radio buttons). It also gives tags to each of control panel components to allow easy location of those components for later use. process_image – runs the experiments and display the results update_sliders – handles UI when there is a dependency between two parameters. Calles to run_process_image if interactive mode is on. run_process_image – change a handle struct to an explicit function call of process_image. Used by Uis which are not sliders and update_sliders function. SetParam – the default callback of all the sliders. Update the GUIs and sliders related strings

18. ‘TopicList’ struct The ‘TopicList’ struct is a struct contains all the topics and their experiments Stored in ‘TopicList.mat’ at the ‘utils’ folder A field example of the TopicList Struct

19. ‘default’ struct The ‘default’ struct is a struct contains all the default signals\images filename, sizes etc of each experiment Stored in ‘Defaults.mat’ at the ‘utils’ folder Filenames are relative to ‘utils’ folder A field example of the default Struct

20. Algorithmic implementation The Matlab program language, while very versatile and convenient, is aimed at vector and matrix processing, and has a major drawback in the form of execution time of loops commonly used when performing local operation on images. To enhance speed, matrix operations are preferred over loops Using im2col function of Maltab loop operations transformed to matrix operations Im2col(im, [WndY, WndX]) function takes an image with size WxH, and creates a new matrix with size of (WndY* WndX)x(W*H). Which means that for 5x5 window, this function creates 24 more replicas of the image. But this cost in memory benefits in execution time Empirical result shows improvement of over 50 times In conclusion, many local image operation were implemented using im2col operation, however, in some cases for memory and educational purposes, a loop implementation was used.

21. Conclusions The “advanced Image Processing Lab” project presents a new platform of teaching the lab It enables a more in-depth learning and experimenting Gives a good comparison between different algorithms and same algorithms with different parameters Can be easily modified to hold more\ different topics