Side-by-Side Image Blurring Filter Comparator

Blurring looks like the simplest operation in image processing, but choosing the right filter matters enormously in practice: media teams smoothing skin in portraits, engineers denoising sensor images, and designers softening backgrounds each need different behavior. The distinction between filters that blur everything and filters that preserve edges is best learned by direct visual comparison. The intern builds a Streamlit application where users upload an image and apply three filters implemented with OpenCV: Gaussian blur, which smooths uniformly using a weighted kernel; median blur, which excels at removing salt-and-pepper noise; and bilateral filtering, which smooths surfaces while keeping edges crisp. The interface presents results side by side against the original, with sliders for kernel size and filter-specific parameters so users can push each technique to its limits and watch where it breaks down. The intern adds guidance explaining which filter suits which situation, from noise removal to stylistic softening. Through the build, the intern learns how convolution and nonlinear filtering actually differ in effect, gains practical OpenCV and Python experience, and demonstrates the ability to create a genuinely useful comparison utility, the sort of tool that helps a media team make an informed choice instead of guessing.