Joint estimation of unknown radiometric data, gain, and offset from thermal images.

Low cost, weight, and size microbolometer-based thermal focal plane arrays are attractive for thermal-imaging applications. Under environmental loads like those in agricultural remote sensing, these cameras tend to suffer from drift in gain and offset with time and thus require constant calibration. Our goal is to skip this step via computational imaging. In a previous work we estimated the unknown offset value and radiometric image of an object, given the calibrated gain, from a pair of successive images taken at two different blur levels, eliminating the need for offset calibration due to temperature variation. Here, we extend our model to a case with unknown gain and offset. We show that these values, as well as the objects' radiometric value, can be found jointly by minimizing a cost function relying on N pairs of blurred and sharp images. The method addresses both space-invariant and space-variant cases. Simulations show promising accuracy with error characterized by root mean squared error of less than 1.6°C.

Radiometric imaging by double exposure and gain calibration.

Due to low cost and small size, uncooled microbolometer-based thermal focal plane arrays are very attractive for radiometry. However, being non-cooled, they suffer from temporally and spatially dependent changes that require constant calibration. While the gain calibration can be reasonably realized by two-point correction, the offset due to internal radiation loads poses a complicated calibration scheme. We present a new computational optics approach that simplifies the essential calibration for temperature offset. Using two successive images of the object taken with different known blur levels, one can eliminate the object term from the image-formation equation, resulting in an equation for the unknown sensor offset. A general algebraic model is presented for the space-variant case followed by solutions using both direct inverse method and iterative solver. The new scheme allows restoration of the radiometric value within 1% error with the direct method, and 0.2% error with the iterative scheme. Account of the influence of realistic lens positioning error on restoration accuracy was given. Results using direct inverse methods for restoring the radiometric values yield restoration error with a good average error of 3.7% and less.