Digital Image Enhancement Techniques: Filter Implementation and Analysis

Digital Image Enhancement techniques are implemented using the classic Lenna image as input, processing it through four distinct filtering approaches. The implementation applies low-pass filtering for noise reduction, median filtering for salt-and-pepper noise removal, along with advanced variants including weighted median and center-weighted median filters. Each filtered result is plotted for visual comparison and evaluation. Image enhancement refers to computational techniques that improve image quality and visual appearance. This process involves applying various filters and algorithms to refine digital images. Low-pass filtering attenuates high-frequency noise components using convolution operations (typically with Gaussian or averaging kernels), resulting in smoother images. Median filtering effectively removes salt-and-pepper noise by replacing each pixel with the median value of its neighborhood, implemented using sliding window operations with numpy or OpenCV. Weighted median and center-weighted median filters represent enhanced versions of standard median filtering. These algorithms incorporate pixel weighting schemes where certain positions in the filtering window carry greater influence, implemented through weight matrices that prioritize central pixels or specific regions. The center-weighted variant particularly emphasizes the central pixel's contribution while maintaining edge preservation capabilities. By visualizing the output images side-by-side, the effectiveness of each enhancement technique becomes directly observable, allowing for quantitative and qualitative assessment of noise reduction, detail preservation, and overall image quality improvement. Implementation typically involves Python with libraries like OpenCV for filter applications and matplotlib for result visualization, with kernel size and weighting parameters adjustable for optimal performance.