Image Preprocessing - Image Denoising Filters

During image preprocessing, a crucial step involves image denoising. This process can be effectively achieved through the application of various filters. In MATLAB, we can implement specific programs to enhance image clarity. The implementation includes several key approaches: 1. Linear Filters: Using functions like imfilter() with predefined kernels such as Gaussian filters for smoothing and noise reduction. The Gaussian filter works by convolving the image with a kernel that follows a normal distribution, effectively reducing high-frequency noise while preserving edges. 2. Median Filters: Implemented through medfilt2() function, which replaces each pixel's value with the median of its neighborhood. This non-linear filter is particularly effective against salt-and-pepper noise while maintaining edge sharpness. 3. Adaptive Filters: Utilizing wiener2() for adaptive noise reduction that adjusts filtering parameters based on local image statistics, providing optimal denoising performance across different regions. The implementation program is clearly structured and easy to understand, effectively removing various types of image noise while significantly improving image quality through proper filter selection and parameter optimization.