ECTI TRANSACTIONS ON COMPUTER INFORMATION TECHNOLOGYVolume 15, No. 03, Month DECEMBER, Year 2021, Pages 278 - 288
Simulation inquisition of noise dissolving algorithm hinge on triple threshold statistical detection filter for random intensity impulse noise situation
Vorapoj Patanavijit, Kornkamol Thakulsukanant
Abstract Download PDFThis primary aim of this philosopher paper investigates the efficacy of the noise dissolving algorithm hinge on TTSD (Triple Threshold Statistical Detection) filter that has been originated since 2018 is one of the highest efficacy for dissolving RIIN (Random-Intensity Impulse Noise), exclusively at dense distribution. As a results, there are three essential contributions: the exhaustive explanation of the TTSD filter algorithm and its computation examples, the calculation simulation of noise apprehension correctness and overall comparative simulation of noise dissolving effectiveness. For TTSD filter, three malleable offsets that are the complementary requirement are employed in the TTSD filter that can adequately resolve the limitation of the antecedent noise dissolving algorithms. The first malleable offset is calculated for determining the noise characteristic of all elements by using the mathematical verification. Next, the second malleable offset is calculated for determining the another noise characteristic by using the normal distribution mathematical verification (the average value and standard deviation value). Later, the third malleable offset is calculated for determining the another noise characteristic by using the quartile mathematical verification (median value). In the simulation inquisition, the bountiful standard portraits that are desecrated by RIIN (Random Intensity Impulse Noise) with many dense distributions are experimented by noise dissolving algorithm hinge on TTSD in both noise segregation and noise dissolving perspective.
TTSD (Triple Threshold Statistical Detection) Filter, Mean Filter, Median Filter, Adaptive Median Filter (AMF), Bilateral Filter (BF)