Gaussian Filter Algorithm for Image Processing

First, we can perform Gaussian filtering on the image. This processing method utilizes a Gaussian kernel function to blur the image, effectively reducing noise and suppressing fine details in the image. The implementation typically involves convolving the image with a Gaussian kernel matrix, where the kernel size and standard deviation (sigma) parameter control the degree of blurring. Larger sigma values produce stronger smoothing effects while smaller values preserve more image details. Next, we can display both the original image and the Gaussian-filtered image side by side. Through comparison of these two images, we can clearly observe the impact of Gaussian filtering on image characteristics. The processed image typically exhibits reduced high-frequency noise while maintaining essential structural information. This comparative visualization approach allows for better understanding and evaluation of Gaussian filtering effects. From an implementation perspective, key functions like GaussianBlur() in OpenCV or fspecial() in MATLAB can be used to generate appropriate Gaussian kernels, followed by convolution operations using imfilter() or filter2() functions.