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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