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THz Signal & Image Processing. Outline. SECTION 1: Alternative configurations Wide beam configuration (synthetic aperture processing) Tomographic configuration ( tomosynthesis processing) SECTION 2: Challenge of data size Enhanced visualisation (Semi-) Automatic defect detection.
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Outline • SECTION 1: Alternative configurations • Wide beam configuration (synthetic aperture processing) • Tomographic configuration (tomosynthesis processing) • SECTION 2: Challenge of data size • Enhanced visualisation • (Semi-) Automatic defect detection
Outline • SECTION 1: Alternative configurations • Wide beam configuration (synthetic aperture processing) • Tomographic configuration (tomosynthesis processing) • SECTION 2: Challenge of data amount • Enhanced visualisation • (Semi-) Automatic defect detection
Wide beam configuration in-depth defects Beam waist depth Top / overhead view depth Top / overhead view material under test material under test focused beam lenses unfocused beam unfocused beam THz THz THz THz THz THz THz THz azimuth azimuth Synthetic Aperture (SA) FMCW Focused FMCW • Small focused beam • Diffraction-limited • Data already focused • Lateral resolution: 1-3 mm • No lenses needed • Wide unfocused beam (16º) • Data is focused in software • Lateral resolution: 4-7 mm
3-D synthetic aperture imagery Inserts - A-sandwich Rohacell core Honeycomb core Near surface 100 GHz – Side A
3-D synthetic aperture imagery Debonds - A-sandwich - honeycomb core Near surface 100 GHz – Side A In depth
3-D synthetic aperture imagery Debonds - A-sandwich - Rohacell core 100 GHz – Side B Near surface 150 GHz – Side B Near surface
3-D synthetic aperture imagery Debonds - C-sandwich - honeycomb core 100 GHz – Side B Near surface 150 GHz – Side B
3-D synthetic aperture imagery Impact (30 J) - Solid laminate - fibre glass 100 GHz – Side A 100 GHz – Side B Near surface
Tomographic configuration Tomosynthesis in transmission mode Data procedure for 3-D imaging • Technique for structural reproduction of object • In THz the beam is close to a Gaussian distribution • Information from reflected or transmitted beam Process of acquisition/reconstruction • Acquisition composed of a set of projections • Modeled by the Radon transform
Tomographic configuration Tomosynthesis in reflection mode View B View C View A • Reconstruction of internal structure with more accuracythan unique reflectionimage • Allow to converge to diffraction limit for BOTHlateral and longitudinal resolution
Tomosynthesis Inserts – Solid laminate – Glass fibre 100 GHz – Side A Images at different depths
Outline • SECTION 1: Alternative configurations • Wide beam configuration (synthetic aperture processing) • Tomographic configuration (tomosynthesis processing) • SECTION 2: Challenge of data size • Enhanced visualisation • (Semi-) Automatic defect detection
Challenge of data size FMCW system 3-D data set – spatial information TDS 3-D data set – spatial information 3-D data set – frequency information CHALLENGE FOR THE OPERATOR • DOTNAC • Enhanced visualisation for flat and curved samples • (Semi-) Automatic defect detection
Defect visualisation Water inclusion- A-sandwich - Honeycomb core Raw image representation Processed image representation
Defect visualisation Impact (30 J) - Solid laminate - fibre glass
Automatic defect detection Inserts - Solid laminate - fibre glass
Automatic defect detection Inserts - Solid laminate - fibre glass
3-D Visualisation In-situ test on radome Imaging with FMCW system 1 screenshot 3-D In-depth viewing
Reconstruction methods: BFP BFP: Backprojection of Filtered Projections W0 corresponds to the projection P filtered by a ramp filter to increase details A is the weight coefficient between the projection (θ;ρ) and the pixel (i; j) We sum the filtered projections crossing each point March 6th 2012
Iterative methods • Makeinitiakguess • Check how wellit corresponds to the measured data • Calculate the differencebetween the result and real measurement • Correct the values • Repeatuntilresultssatisfying • SART: SimultaneousAlgebraic Reconstruction Technique • OSEM (Ordered Subsets Expectation Method)