Automatic Image Blending Algorithm Based on SAM and DINO

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.