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Visible and long-wave infrared (LWIR) image fusion is essential for robust imaging, yet balancing natural color and thermal detail remains challenging. This study proposes a novel framework integrating a selective pre-enhancement module, an advanced fusion network, and a Palette Denoising Diffusion Probabilistic Model (DDPM) for high-quality synthesis in no-reference environments. The fusion network employs an encoder–decoder architecture with Residual-in-Residual Dense Blocks (RRDBs) and a Convolutional Block Attention Module (CBAM) to extract discriminative multi-level features. Spatially Adaptive Normalization (SPADE) and a Visibility Deficiency Mask (VDF) are incorporated to adaptively compensate for information-poor regions while preserving modality-specific characteristics. The fused output is subsequently refined by a conditional DDPM to restore fine-grained textures and suppress noise. Finally, post-processing enhances global contrast and color naturalness while mitigating thermal artifacts. Experimental results demonstrate that the proposed method effectively reduces over-brightness and improves detail preservation in diverse nighttime scenarios.
Son et al. (Wed,) studied this question.