SFX-GAN: sustainable and explainable multi-modal spectral fusion for image dehazing in complex systems

Image dehazing plays a critical role in understanding complex real-world systems such as transportation, environmental monitoring, and healthcare imaging, where haze significantly degrades visual information and hinders decision-making. Traditional deep learning models emphasize accuracy but often neglect interpretability and resource efficiency, limiting their applicability in sustainable multi-modal fusion frameworks. We introduce SFX-GAN, a Sustainable and eXplainable Frequency-domain GAN that employs a novel channel-wise spectral fusion strategy, treating low, mid, and high-frequency components across RGB channels as complementary modalities. This multi-modal spectral integration enables the model to capture both global context and fine-grained structural cues, facilitating more reliable restoration of haze-free images. For interpretability, SFX-GAN incorporates a Concept Bottleneck Layer and Grad-CAM supervision, aligning network attention with haze-affected regions and enhancing trust in automated dehazing for safety-critical applications. Designed with sustainability in mind, SFX-GAN leverages frequency-domain processing to deliver efficient inference with reduced computational overhead. Experiments on RESIDE-SOTS, NH-HAZE, I-HAZE, O-HAZE, and D-HAZY datasets demonstrate state-of-the-art performance under both full-reference (PSNR, SSIM) and no-reference (NIQE, BRISQUE, FADE, SSEQ) metrics. Ablation studies confirm the effectiveness of each module in the fusion pipeline. Overall, SFX-GAN contributes to the intelligent fusion of spectral-spatial modalities for complex imaging systems, advancing responsible and explainable AI aligned with SDG 9 (Industry, Innovation, and Infrastructure) and SDG 11 (Sustainable Cities and Communities).