In recent years, image blending has gained popularity for its ability to create visually stunning content. However, the current image blending algorithms mainly have the following problems: manually creating image blending masks requires a lot of manpower and material resources; image blending algorithms cannot effectively solve the problems of brightness distortion and low resolution. To this end, we propose a new image blending method with automatic mask generation: it combines semantic object detection and segmentation with mask generation to achieve deep blended images based on our proposed new saturation loss and two-stage iteration of the PAN algorithm to fix brightness distortion and low-resolution issues. Results on publicly available datasets show that our method outperforms other classical image blending algorithms on various performance metrics, including PSNR and SSIM.
@article{arxiv.2306.05382,
title = {Image Blending Algorithm with Automatic Mask Generation},
author = {Haochen Xue and Mingyu Jin and Chong Zhang and Yuxuan Huang and Qian Weng and Xiaobo Jin},
journal= {arXiv preprint arXiv:2306.05382},
year = {2023}
}