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Research Papers

Error Reduction in Infrared Thermography by Multiframe Super-Resolution

[+] Author and Article Information
Aditya Chandramohan

Cooling Technologies Research Center,
School of Mechanical Engineering
Purdue University,
West Lafayette, IN 47907
e-mail: chandr22@purdue.edu

Sara K. Lyons

Cooling Technologies Research Center,
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: lyons20@purdue.edu

Justin A. Weibel

Cooling Technologies Research Center,
School of Mechanical Engineering
Purdue University,
West Lafayette, IN 47907
e-mail: jaweibel@purdue.edu

Suresh V. Garimella

Cooling Technologies Research Center,
School of Mechanical Engineering,
Purdue University,
West Lafayette, IN 47907
e-mail: sureshg@purdue.edu

1Corresponding author.

Contributed by the Electronic and Photonic Packaging Division of ASME for publication in the JOURNAL OF ELECTRONIC PACKAGING. Manuscript received April 11, 2018; final manuscript received August 24, 2018; published online October 1, 2018. Assoc. Editor: Mehdi Asheghi.

J. Electron. Packag 140(4), 041008 (Oct 01, 2018) (8 pages) Paper No: EP-18-1028; doi: 10.1115/1.4041360 History: Received April 11, 2018; Revised August 24, 2018

Accurate temperature measurement techniques are critical for monitoring hotspots that induce thermal stresses in electronics packages. Infrared thermography is a popular nonintrusive method for emissivity mapping and measuring surface temperature distribution, but is often impeded by the low native resolution of the camera. A promising technique to mitigate these resolution limits is multiframe super-resolution, which uses multiple subpixel shifted images to generate a single high-resolution image. This study quantifies the error reduction offered by multiframe super-resolution to demonstrate the potential improvement for infrared imaging applications. The multiframe super-resolution reconstruction is implemented using an algorithm developed to interpolate the sub-pixel-shifted low-resolution images to a higher resolution grid. Experimental multiframe super-resolution temperature maps of an electronic component are measured to demonstrate the improvement in feature capture and reduction in aliasing effects. Furthermore, emissivity mapping of the component surface is conducted and demonstrates a dramatic improvement in the temperature correction by multiframe super-resolution. A sensitivity analysis is conducted to assess the effect of registration uncertainty on the multiframe super-resolution algorithm; simulated images are used to demonstrate the smoothing effect at sharp emissivity boundaries as well as improvement in the feature size capture based on the native camera resolution. These results show that, within the limitations of the technique, multiframe super-resolution can be an effective approach for improving the accuracy of emissivity-mapped temperature measurements.

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Figures

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Fig. 1

Schematic diagram of the experimental setup used for the multiframe super-resolution infrared measurements

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Fig. 2

(a) Photograph of the computer memory card used to demonstrate multiframe super-resolution reconstruction. (b) An inset photograph of a serpentine wire trace feature with (c) the corresponding low-resolution, and (d) multiframe super-resolution infrared intensity maps.

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Fig. 3

(a) Uncorrected and (b) emissivity-corrected low-resolution temperature maps, as well as (c) uncorrected and (d) corrected multiframe super-resolution maps of a portion of the component shown in Fig. 2

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Fig. 4

Emissivity maps generated using the (a) low-resolution and (b) super-resolution images

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Fig. 5

Maximum deviation based on a sensitivity analysis of input sensitivity values

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Fig. 6

The (a) smooth wave and (b) sharp band scenes of nondimensionalized temperature are shown above horizontal line plots of the scene, multiframe super-resolution image, and low-resolution image

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Fig. 7

The absolute errors for the low-resolution and super-resolution images of the nondimensionalized temperature are shown for the (a) sharp bands and (b) smooth wave scenes

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Fig. 8

The difference between the error of the low-resolution and multiframe super-resolution images of the nondimensionalized temperature plotted as a function of the pixel-to-feature size ratio for the (a) sharp bands and (b) smooth wave scenes. The results with 10% maximum image noise are plotted for the (c) sharp bands and (d) smooth waves. The insets in (a) illustrate example pixel size relative to the band feature.

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