  We introduce DeepIR, a new thermal image processing framework that combines
physically accurate sensor modeling with deep network-based image
representation. Our key enabling observations are that the images captured by
thermal sensors can be factored into slowly changing, scene-independent sensor
non-uniformities (that can be accurately modeled using physics) and a
scene-specific radiance flux (that is well-represented using a deep
network-based regularizer). DeepIR requires neither training data nor periodic
ground-truth calibration with a known black body target--making it well suited
for practical computer vision tasks. We demonstrate the power of going DeepIR
by developing new denoising and super-resolution algorithms that exploit
multiple images of the scene captured with camera jitter. Simulated and real
data experiments demonstrate that DeepIR can perform high-quality
non-uniformity correction with as few as three images, achieving a 10dB PSNR
improvement over competing approaches.
