Median Filter Optimization and Sharpening Techniques to Improve Digital Image Quality - Riko Prananda Prayugo; Lailan Sofinah Harahap (SEAN Institute, 2025)
The quality of digital images often degrade due to noise disturbances, especially impulsive noise such as salt and pepper. The aim of this study is to optimize the use of the median filter as a denoising technique and to combine it with a sharpening method to enhance the sharpness and clarity of image structures. The proposed approach involves applying an adaptive median filter reduce noise while preserving edge details, followed by a convolution kernel based sharpening technique to further emphasize visual features. The performance of the method is evaluated by compare the Peak Signal-to-Noise Ratio (PSNR) and the Structural Similarity Index Measure (SSIM) between the processed images and the original images. The experimental results demonstrated that this combined approach significantly improve both PSNR and SSIM values after filtering and sharpening, indicating that the synergy between median filtering and sharpening effectively restore the visual quality of digital images. These findings can serve as a foundation for the development of adaptive image preprocessing systems to handle impulsive noise.