Shortwave infrared and visible light image fusion method based on dual discriminator GAN

In a tactical warfare setting, the efficacy of target detection becomes profoundly compromised due to prevalent environmental factors such as smoke, dust, and atmospheric interference. Such impediments invariably undermine the precision and reliability of identifying pivotal targets, thereby precipitating potentially dire ramifications. Remarkably, short-wave infrared technology has exhibited unparalleled proficiency in elucidating target attributes even amidst challenging conditions characterized by smoke, fog, or haze. Against this backdrop, the present study delineates a pioneering algorithmic framework that seamlessly amalgamates the imperatives of image registration and fusion. This is achieved through the deployment of an advanced dual-discriminator Generative Adversarial Network (GAN), specifically tailored for amalgamating short-wave infrared and visible light imagery within smoke-obscured contexts. Our methodology commences with the introduction of an augmented Speeded-Up Robust Features (SURF) algorithm, meticulously designed to rectify inherent misalignments within the input imagery. Subsequent enhancements encompass the refinement of the generator's loss function and the integration of a multi-scale convolutional kernel, thereby facilitating the extraction and amalgamation of a more expansive array of salient features. This concerted effort culminates in the elevation of image fusion quality. To corroborate the efficacy and robustness of our proposed framework, rigorous validation procedures were conducted utilizing a meticulously curated dataset comprising short-wave infrared and visible light images. Empirical evaluations, encompassing both subjective and objective comparative analyses, unequivocally affirm the superior performance metrics of our fusion network. Specifically, our methodology surpasses alternative fusion techniques across multiple dimensions, including visual fidelity, perceptual quality, and structural congruence of synthesized images.