[vc_empty_space][vc_empty_space]
Digital image restoration using posterior distribution and updating pixel by self threshold
Adi K.a,b, Mengko T.L.R.a, Suksmono A.B.a, Danudirdjo D.a
a School of Electrical Engineering and Informatics, Institut Teknologi Bandung Jalan, Indonesia
b Department of Physics, Universitas Diponegoro, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm has been applied to simulate digital image restoration using posterior distribution and pixels update with self threshold method. This method was implemented to Lena image degraded with 0.15 th order salt and pepper noise by the variation of temperature at 1.5, 2.5 and 4.5. The result of digital image restoration in gray scale at temperature 1.5 is good image restored with Δ SNR 13.072 dB. In this simulation, the number of Markov chains (1000 chains) and iteration (800 iteration) are fixed parameter. © 2006 ICASE.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text]Digital image restoration,Metropolis Hastings Markov Chain Monte Carlo (MHMCMC) algorithm,Posteriori distribution,Self threshold[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]MHMCMC algorithm,Posteriori distribution,Self threshold[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/SICE.2006.314638[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]