@inproceedings{4e6836997762498aaffb178a3017bbe5,
title = "Image Blending Algorithm with Automatic Mask Generation",
abstract = "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, while 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.",
keywords = "Image Blending, Image Segmentation, Mask Generation, Object Detection",
author = "Haochen Xue and Mingyu Jin and Chong Zhang and Yuxuan Huang and Qian Weng and Xiaobo Jin",
note = "Publisher Copyright: {\textcopyright} 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 30th International Conference on Neural Information Processing, ICONIP 2023 ; Conference date: 20-11-2023 Through 23-11-2023",
year = "2024",
doi = "10.1007/978-981-99-8132-8_18",
language = "English",
isbn = "9789819981311",
series = "Communications in Computer and Information Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "234--248",
editor = "Biao Luo and Long Cheng and Zheng-Guang Wu and Hongyi Li and Chaojie Li",
booktitle = "Neural Information Processing - 30th International Conference, ICONIP 2023, Proceedings",
}