A Pervasive Numerical Investigation of HDF Discrimination for Identifying and Enhancing Impulsive Noise Photograph
Darun Kesrarat, Vorapoj Patanavijit, Kanabadee Srisomboon, Wilaiporn Lee, Kornkamol Thakulsukanant · Journal of Lifestyle and SDGs Review · 2025
Objective: This study aims to investigate the optimal cluster size and the optimal HDT parameter of the noise elimination approach founded on HDT (Hard Decision Threshold) discrimination on a lot total of experimental photographs on CTII for maximum capability. Theoretical Framework: The theoretical framework is founded on the dissimilarity values of the cluster pixels are significantly different neighborhood pixels. However, the capability of the noise elimination approach founded on HDT ultimately depends on the optimal cluster size and the optimal HDT parameter. Method: The noise elimination approach founded on HDT is investigated in both quantitative (in PSNR or Peak Signal to Noise Ratio) and qualitative (visionary). This experiment investigates on a lot of photographs (Lena, Pepper and Pentagon) in CTII (Constant Tension Impulsive Irregularity) at uniform distribution and cluster distribution. Results and Discussion: First, the results of the study based on HDT parameters from Lena, Pepper and Pentagon shows that the optimal HDT parameter is 0.2±0.1 approximately. Next, from numerical outcome on a lot of photographs, the HDT noise elimination approach is capable of the remarkable quality photographs with the first-rate PSNR opposed with former reputable approaches for example Gaussian/Mean filter (MF), Median filter (SMF) and Adaptive Median filter (AMF) for wholly CTII.