The field of image blending has gained significant popularity in recent years due to its ability to create visually stunning content. The main objective of image blending is to merge an object from one image onto another seamlessly, with minor masking adjustments. With the recent development of SAM, which can detect and segment targets in images automatically. Our approach (1) combines semantic object detection and segmentation with corresponding mask generation to automatically fuse images and (2) introduces the use of PAN for further quality enhancement during the fusion process. Our approach surpasses many classical visual fusion models in various performance indicators such as PSNR, SSIM, and Realism. Notably, our process is highly efficient and speedy, making it widely applicable in industrial settings. This new process has the potential to revolutionize visual content creation and improve productivity across various industries.

翻译：图像融合领域近年来因能够生成视觉惊艳的内容而广受欢迎。图像融合的主要目标是将一幅图像中的对象无缝融合到另一幅图像中，仅需少量的掩膜调整。随着最近能够自动检测和分割图像中目标的SAM模型的发展，我们的方法：（1）结合语义目标检测与分割及对应的掩膜生成，实现图像的自动融合；（2）引入PAN用于融合过程中的进一步质量提升。我们的方法在PSNR、SSIM和真实感等多项性能指标上超越了众多经典视觉融合模型。值得注意的是，我们的流程高效且快速，使其在工业场景中具有广泛适用性。这一新流程有望彻底改变视觉内容创作，并提升各行业的生产效率。