서수민 박사과정 논문 IEEE ACCESS 저널 게재 승인

서수민 박사과정생의 논문이 IEEE ACCESS 저널에 accept되었습니다. 축하합니다! Title: UPSNet: Unsupervised Pan-Sharpening Network with Registration Learning between Panchromatic and Multi-Spectral Images Authors: Soomin Seo, Jae-Seok Choi, Jaehyup Lee, Hyun-Ho Kim, Doochun Seo, Jaeheon Jeong, and Munchurl Kim Abstract:

Recent advances in deep learning have shown impressive performances for pan-sharpening. Pan-sharpening is the task of enhancing the spatial resolution of a multi-spectral (MS) image by exploiting the high-frequency information of its corresponding panchromatic (PAN) image. Many deep-learning-based pan-sharpening methods have been developed recently, surpassing the performances of traditional pan-sharpening approaches. However, most of them are trained in lower scales using misaligned PAN-MS training pairs, which has led to undesired artifacts and unsatisfying visual quality. In this paper, we propose an unsupervised learning framework with registration learning for pan-sharpening, called UPSNet. UPSNet can be effectively trained in the original scales, and implicitly learns the registration between PAN and MS images without any dedicatedly designed registration module involved. Additionally, we design two novel loss functions for training UPSNet: a guided-filter-based color loss between network outputs and aligned

MS targets; and a dual-gradient detail loss between network outputs and PAN inputs. Extensive experimental results show that our UPSNet can generate pan-sharpened images with remarkable improvements in terms of visual quality and registration, compared to the state-of-the-art methods.