PSGAN: Pseudo-Sensing GAN for Infrared and Low-Light Visible Image Fusion

Infrared and visible image fusion, as a pivotal image processing technique, aims to integrate complementary information from heterogeneous source images into a high-quality fused representation. However, the intrinsic domain discrepancy between infrared and visible modalities, coupled with the susceptibility to low-light environments, significantly hampers the fusion process and degrades the quality of the results. To address these limitations, this paper proposes PSGAN, a triple-discriminator generative adversarial network incorporating a pseudo-sensing mechanism. Specifically, our method synergistically integrates the distributional alignment capability of generative models with a pseudo-sensing mechanism that simulates the inverse process of perceptual imaging. This approach effectively alleviates the domain discrepancy across disparate imaging physics, empowering the network to extract robust, cross-modal aligned features. Furthermore, an additional illumination discriminator is incorporated into the conventional dual-adversarial framework to guide the fused images toward achieving optimal luminance and contrast, even in extreme darkness. Finally, to comprehensively quantify the performance of fusion algorithms across multiple dimensions, this paper develops a multi-criteria evaluation framework based on Dempster–Shafer (D-S) evidence theory. Experimental results demonstrate that the PSGAN algorithm achieves superior fusion accuracy and strong generalization capability, while simultaneously providing significant performance gains for downstream detection tasks.