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Performance analysis of CS method for image reconstruction on microwave tomography
Imanda D.K.a, Munir A.a
a School of Electrical Engineering Informatics Institut Teknologi Bandung, Radio Telecommunication and Microwave Laboratory, Bandung, 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]© 2020 IEEE.This paper describes the performance analysis of image reconstruction on microwave tomography using the compressive sensing (CS) method. The investigation is carried out by processing an image of tree trunk object with different pixel sizes, i.e. 256×256, 128×128, and 64×64. Performance comparison are assessed using mean square error (MSE) and structural similarity index measure (SSIM). The results of image reconstruction using CS method with 256×256 pixels have the best MSE and SSIM with values of 0.001 and 0.944, respectively. Meanwhile, the image reconstruction with 128×128 pixels is not much different from the one with 256×256 pixels which produces the MSE value of 0.001 and the SSIM value of 0.899. Furthermore, the result of image reconstruction with 64×64 pixels is worst among others and yields a blurry image. However, it has MSE and SSIM values of 0.001 and 0.837, respectively, with fastest computation time compared to other pixel sizes.[/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]Blurry images,Compressive sensing,Computation time,Microwave tomography,Performance analysis,Performance comparison,Pixel size,Structural similarity index measures (SSIM)[/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]Compressive sensing (CS),Image reconstruction,Incoherent,Microwave tomography,Sparsity[/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/TSSA51342.2020.9310844[/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]