SSRA: Semantic Segmentation-Guided Region-Attention Colorization Method

Infrared image colorization has witnessed notable advances in recent years; however, existing methods still suffer from color distortions, edge blurring, and artifacts, particularly in complex scenes. To mitigate these issues, we propose a Semantic Segmentation-Guided Region-Attention (SSRA) framework, which enhances colorization fidelity with a stronger emphasis on semantically salient regions. Specifically, we employ the Segment Anything Model (SAM2) to produce high-quality semantic masks, enabling regional self-attention to operate within consistent object boundaries. This design effectively suppresses background interference and facilitates more precise color reconstruction in foreground regions. Furthermore, a focal region loss is introduced to adaptively weight reconstruction errors based on regional importance, thereby directing the model’s representational capacity towards critical areas such as vehicles, pedestrians, and road infrastructure. Extensive experiments on two drone-acquired datasets validate the efficacy of our approach, demonstrating superior performance in both quantitative metrics and qualitative assessments, with notable improvements in structural detail preservation and semantic-aware color consistency.