Nonuniformity Correction Algorithm for Infrared Image Sequences Based on Spatiotemporal Total Variation Regularization

In infrared detectors, the readout circuits usually cause horizontal or vertical streak noise, whereas the infrared focal plane arrays experience triangular nonuniform fixed-pattern noise. In addition, imaging devices suffer from optically relevant fixed-pattern noise owing to the temperature. When the infrared camera is in orbit, it is affected by the photon effect, temperature change, and time drift. This makes the nonuniformity correction coefficients pertaining to the ground no longer applicable, resulting in the degradation of the nonuniformity correction effect. The existing methods are not fully applicable to triangular fixed-pattern noise or the fixed-pattern noise caused by detector optics. To address this situation, this paper proposes a nonuniformity correction method, namely infrared image sequences based on the optimization of L2,1 group sparsity in the spatiotemporal domain. We established a nonuniformity correction model of differential operators in the spatiotemporal domain for infrared image sequences by applying the time-domain differential operator constraints to the images to denoise the image. This enables the adaptive correction of the nonuniformity of the above types of noise. We demonstrate that the proposed method is effective for triangular nonuniform and optically induced fixed-pattern noises. The proposed method was extensively evaluated using publicly available datasets and datasets containing image sequences of different scenes captured by a high-resolution infrared camera of the Qilu-2 satellite. The method has high robustness and good processing results with effective ghost suppression and significant reduction of nonuniform noise.