Noise Level Estimation for Digital Images Using Local Statistics and Its Applications to Noise Removal

Khmag, Asem; Ghoul, Sami; Al-Haddad, S. A. R.; Kamarudin, Noraziahtulhidayu

doi:10.12928/telkomnika.v16i4.9060

Cited by 1 publication

(1 citation statement)

References 14 publications

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“…In the last three decades, many algorithms [1][2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20] that hinge on distinctive techniques have been investigated and evolved for noise apprehension and noise dissolving [10][11][12][13][14][15][16][17][18][19][20] in order to restore profaned portraits because there are great requirements for high quality portraits, which have been used in modern complex applications [1][2][3][4] such as medical imaging [7][8][9], remote sensing [6], super resolution (SR) [5],…”

Section: Sivve Noisementioning

confidence: 99%

Simulation Inquisition of Noise Dissolving Algorithm Hinge on TTSD filter for RIIN Situation

Patanavijit

Thakulsukanant

2021

ECTI-CIT

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This 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.

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