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.