{"type":"ScholarlyArticle","title":"PanComplex: Leveraging Complex-Valued Neural Networks for Enhanced Pansharpening","authors":[{"name":"Chunhui Luo","slug":"chunhui-luo","given":"Chunhui","family":"Luo","affiliation":""},{"name":"Dong Li","slug":"dong-li","given":"Dong","family":"Li","affiliation":""},{"name":"Xiaoliang Ma","slug":"xiaoliang-ma","given":"Xiaoliang","family":"Ma","affiliation":""},{"name":"Xin Lu","slug":"xin-lu","given":"Xin","family":"Lu","affiliation":""},{"name":"Zhiyuan Wang","slug":"zhiyuan-wang","given":"Zhiyuan","family":"Wang","affiliation":""},{"name":"Jiangtong Tan","slug":"jiangtong-tan","given":"Jiangtong","family":"Tan","affiliation":""},{"name":"Xueyang Fu","slug":"xueyang-fu","given":"Xueyang","family":"Fu","affiliation":""}],"abstract":"Pansharpening combines panchromatic and low-resolution multispectral images to generate high-resolution multispectral images. Previous studies have explored the connection between pansharpening and the frequency domain, but mostly in the real-valued domain, leaving the complex domain relatively unexplored. To redefine the pansharpening task, we propose a complex-valued spatial-frequency dual-domain framework, PanComplex. To achieve this, we first establish complex representations and introduce basic complex operators tailored to pansharpening, enabling the transformation of multispectral real-valued signals into the complex domain for learning. We then model both spatial and frequency branches to capture global frequency features and local spatial features comprehensively. Finally, we employ a complex-based interaction module to fuse the spatial and frequency features, achieving complementary information across both domains. By using the representation power of the complex domain, PanComplex effectively extracts complementary features from PAN and MS images, thereby enhancing pansharpening performance. Experiments on multiple datasets demonstrate that our method achieves optimal performance with the fewest parameters and exhibits strong generalization ability to other tasks. The source code for this work is publicly available at https://github.com/lch-ustc/PanComplex.","doi":"10.24963/ijcai.2025/190","url":"https://ijcai.org/proceedings/2025/190","pdf":"https://ijcai.org/proceedings/2025/0190.pdf","markdown":"https://ijcai.org/proceedings/2025/190.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/190","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Computer Vision","track":"Main Track","number":190,"pages":{"first":1702,"last":1710},"keywords":["Computer Vision: CV: Other","Computer Vision: CV: Low-level Vision"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}