GUI Programming for Image Processing Using MATLAB Toolbox

This project implements image processing GUI programming using MATLAB toolbox. Key features and implementation approaches include: Image reading and saving functionality, enabling users to conveniently load and store images using MATLAB's imread() and imwrite() functions. Design of graphical user interface allowing users to adjust image brightness and contrast arbitrarily according to their requirements. The implementation utilizes MATLAB's guidata and callback functions to create interactive sliders for real-time parameter adjustment, with immediate visual comparison between original and modified images. Development of GUI components for mouse-based region of interest (ROI) selection, employing MATLAB's impoly or imrect functions to capture designated areas. The selected regions can be displayed and saved separately using matrix cropping operations. Programming implementation for image scaling operations (integer multiples) on user-selected regions using nearest-neighbor and bilinear interpolation algorithms. The code compares interpolation effects through side-by-side visualization, implementing nearest-neighbor using direct pixel mapping and bilinear interpolation through weighted averaging of surrounding pixels. Histogram statistics and equalization functionality that displays image histogram distributions using MATLAB's imhist function. Histogram equalization is implemented using histeq() to enhance image contrast, with comparative analysis of pre- and post-processing effects. Capability to add various types of noise (Gaussian, salt-and-pepper) to images using imnoise() function, followed by denoising through multiple filtering algorithms (median filtering, Wiener filtering). The interface displays processed results for comparative analysis of different filtering techniques. Spectral processing functionality that analyzes image frequency spectra using 2D Fourier transform (fft2) and displays spectral diagrams for user reference. Design of Butterworth low-pass filter for image low-pass filtering processing, implementing frequency domain filtering through butter() function and frequency multiplication, with real-time result display. Design of Gaussian high-pass filter for image high-pass filtering processing, utilizing fspecial() function to create Gaussian filters and applying them in frequency domain for edge enhancement and detail preservation. These represent the core functionalities and implementation approaches of this GUI programming design for image processing applications.