A Model-Free Method for Irregular Bias Field Correction in Infrared Images

Due to the thermal imaging mechanism, infrared images are inevitably contaminated by thermal radiation bias fields. Correcting this low-frequency non-uniformity is challenging because the bias field is difficult to separate from the low-frequency scene content. Existing methods fit parametric surface models to estimate bias fields. However, variations in camera operating states and environment often lead to irregular bias field that such surface models cannot accurately capture. This paper proposes a novel model-free method for irregular bias field correction. The proposed method utilizes the normalized deconvolution module to restore the clean image in the probability density function (PDF) domain, in which the PDF of the bias field can be approximated by a series of specific Gaussian kernel functions. Accordingly, the complete bias field could be obtained gradually in the PDF domain by incremental updating. To transform the image PDF back to the intensity domain, the corresponding conditional expectation is computed in this paper. Note that the proposed method does not require any explicit parametric modeling of the bias field. Therefore, the proposed method is able to correct irregular bias fields effectively. Extensive experiments demonstrate that the proposed model-free method outperforms existing methods in synthesized and real infrared images.