This letter proposes an algorithm to generate pseudo-polarization images under situations with limited polarization image samples. The algorithm is inspired by the principle of polarimetric imaging with DoFP and utilizes a specially designed 2×2 pseudo-polarization filter to generate two sets of orthogonal pseudo-polarization images. Furthermore, the Gaussian filter layering and image gradient-based feature search method are employed for the simulated generation of polarization features for both specular and diffuse reflections. Experiment results indicate high correspondence between the generated pseudo-polarization and real polarization images. The method effectively simulates polarization images acquired by the DoFP polarimeter under different conditions.

The fusion of infrared (IR) and visible (VIS) images aims to capture complementary information from diverse sensors, resulting in a fused image that enhances the overall human perception of the scene. However, existing fusion methods face challenges preserving diverse feature information, leading to cross-modal interference, feature degradation, and detail loss in the fused image. To solve the above problems, this paper proposes an image fusion method based on the infrared target mask and bimodal feature extraction strategy, termed IBFusion. Firstly, we define an infrared target mask, employing it to retain crucial information from the source images in the fused result. Additionally, we devise a mixed loss function, encompassing content loss, gradient loss, and structure loss, to ensure the coherence of the fused image with the IR and VIS images. Then, the mask is introduced into the mixed loss function to guide feature extraction and unsupervised network optimization. Secondly, we create a bimodal feature extraction strategy and construct a Dual-channel Multi-scale Feature Extraction Module (DMFEM) to extract thermal target information from the IR image and background texture information from the VIS image. This module retains the complementary information of the two source images. Finally, we use the Feature Fusion Module (FFM) to fuse the features effectively, generating the fusion result. Experiments on three public datasets demonstrate that the fusion results of our method have prominent infrared targets and clear texture details. Both subjective and objective assessments are better than the other twelve advanced algorithms, proving our method's effectiveness.