MATLAB Implementation of Single-Scale Self-Quotient Image Algorithm

This MATLAB program implements the single-scale self-quotient image algorithm for image processing and analysis. The function employs logarithmic transformation and Gaussian filtering to separate illumination and reflectance components, enabling effective shadow removal and detail enhancement. Users can utilize this implementation to apply the self-quotient algorithm to images at different scales through parameter adjustment. The MATLAB code provides a straightforward yet efficient approach for processing and analyzing image data, incorporating key functions like imread for image loading, fspecial for Gaussian kernel generation, and imfilter for convolution operations. Through this implementation, users can perform rapid and accurate image processing to obtain desired results with optimized computational efficiency. The program offers various configurable parameters and options, including filter size selection, sigma values for Gaussian smoothing, and normalization techniques to accommodate diverse image processing requirements. The algorithm's core functionality involves calculating the quotient between the original image and its smoothed version to eliminate lighting variations while preserving essential texture information. This implementation is valuable for both professionals and beginners, providing clear insights into the self-quotient image algorithm's practical application through well-commented code structure and modular design. The program includes error handling for invalid inputs and supports multiple image formats through MATLAB's image processing toolbox integration.