Non-uniformity correction algorithm for DoFP adapted to integration time variations.

Division of the focal plane (DoFP) polarization detector is a pivotal technology in real-time polarization detection. This technology integrates a micropolarization array (MPA) onto the conventional focal plane, introducing a more intricate non-uniformity than traditional focal plane detectors. Current non-uniformity correction algorithms for DoFP are difficult to adapt to changes in integration time and perform poorly in low-polarization scenarios. Analyzing the characteristics of DoFP, formulating a pixel response model, and introducing an adaptive non-uniformity correction algorithm tailored for varying integration time. The DoFP analysis vectors are decomposed into average polarization response and unit analysis vectors for correction separately to improve the performance of the correction algorithm in different polarization scenarios. The performance of modern correction algorithms was tested and evaluated using standard uniform images, and the proposed method outperformed existing algorithms in terms of polarization measurement accuracy under the root mean square error (RMSE) metric. Moreover, in natural scene images, our proposed algorithm shows favorable visual effects and distinguishes itself from its superior stability amid changes in the integration time.

Demosaicking DoFP images using edge compensation method based on correlation.

With the development of nanotechnology, the division of focal plane (DoFP) infrared polarization imaging system with real-time imaging has matured. Meanwhile, the demand for real-time acquisition of polarization information is growing, but the super-pixel structure of the DoFP polarimeter will bring instantaneous field of view (IFoV) errors. Existing polarization demosaicking methods cannot satisfy both accuracy and speed in terms of efficiency and performance. According to the characteristics of DoFP, this paper proposes an edge compensation demosaicking method by analyzing the channel correlations of polarized images. The method performs demosaicing in the differential domain, and the proposed method's performance is verified by comparison experiments using synthetic and authentic polarized images in the near-infrared (NIR) band. The proposed method outperforms the state-of-the-art methods in terms of accuracy and efficiency. It achieves an average peak signal-to-noise ratio (PSNR) improvement of 2 db on public datasets compared to current state-of-the-art methods. A typical 768 × 1024 specification short-wave infrared (SWIR) polarized image can be processed in 0.293s on the Intel Core i7-10870 H CPU, and the technique significantly outperforms various existing demosaicking methods.