Gaussian Low Pass and Gaussian High Pass Filters

Gaussian Low Pass and Gaussian High Pass Filters are widely used image processing techniques. In MATLAB, these filters can be efficiently implemented for image smoothing, noise reduction, and sharpening operations. The Gaussian Low Pass Filter effectively removes high-frequency noise from images by applying a weighted average based on Gaussian distribution, resulting in smoother image outputs - typically implemented using functions like fspecial('gaussian') or imgaussfilt() with specified sigma parameters. Conversely, the Gaussian High Pass Filter enhances image edges and details by subtracting the low-frequency components from the original image, which can be achieved through frequency domain filtering using fft2 and ifft2 functions or spatial domain convolution operations. By utilizing these filters with appropriate parameter tuning, image quality can be significantly improved, allowing better visualization of details and features through selective frequency manipulation. The implementation typically involves defining filter size, calculating Gaussian kernels, and applying convolution operations using imfilter or custom matrix operations.