MATLAB Implementation of Image DOG (Difference of Gaussians) Processing

This is a MATLAB-implemented image DOG (Difference of Gaussians) processing program designed to generate Difference of Gaussians maps from input images. The program enhances image details and contrast by computing the Gaussian difference for each pixel, resulting in sharper and more layered visual outputs. The DOG technique, a fundamental image processing method, emphasizes edges and textures through sequential Gaussian filtering and differential operations applied to the original image. The implementation typically involves these key steps: - Applying two Gaussian filters with different standard deviations (σ1 and σ2) to create blurred image versions - Calculating the pixel-wise difference between the two filtered images - Utilizing MATLAB's imgaussfilt() function for efficient Gaussian filtering - Employing matrix subtraction operations for differential computation Through this program, users can gain deeper insights into image processing principles and methodologies while enhancing their MATLAB programming skills. The code demonstrates practical applications of convolution operations and spatial filtering techniques commonly used in computer vision and image analysis workflows.