In latest years, deep learning has gained a leading role in the pansharpening of multiresolution images. Given the lack of ground truth data, most deep learning-based methods carry out supervised training in a reduced-resolution domain. However, models trained on downsized images tend to perform poorly on high-resolution target images. For this reason, several research groups are now turning to unsupervised training in the full-resolution domain, through the definition of appropriate loss functions and training paradigms. In this context, we have recently proposed a full-resolution training framework which can be applied to many existing architectures. Here, we propose a new deep learning-based pansharpening model that fully exploits the potential of this approach and provides cutting-edge performance. Besides architectural improvements with respect to previous work, such as the use of residual attention modules, the proposed model features a novel loss function that jointly promotes the spectral and spatial quality of the pansharpened data. In addition, thanks to a new fine-tuning strategy, it improves inference-time adaptation to target images. Experiments on a large variety of test images, performed in challenging scenarios, demonstrate that the proposed method compares favorably with the state of the art both in terms of numerical results and visual output. Code is available online at https://github.com/matciotola/Lambda-PNN.

翻译：近年来，深度学习在多分辨率图像的全色锐化领域占据了主导地位。由于缺乏真实地物数据，大多数基于深度学习的方法在降分辨率域中进行监督训练。然而，在低分辨率图像上训练的模型往往在高分辨率目标图像上表现欠佳。为此，多个研究团队转向全分辨率域中的无监督训练，通过定义恰当的损失函数和训练范式来实现。在此背景下，我们近期提出了一种可应用于多种现有架构的全分辨率训练框架。本文提出了一种新的基于深度学习的全色锐化模型，该模型充分挖掘了该方法的潜力，并实现了前沿性能。除采用残差注意力模块等架构改进外，本模型还设计了一种新型损失函数，可共同提升全色锐化数据的光谱与空间质量。此外，得益于新的微调策略，模型在推理阶段对目标图像的适应能力得到增强。在挑战性场景下针对大量测试图像进行的实验表明，所提方法在数值结果和视觉输出方面均优于现有技术水平。代码发布于 https://github.com/matciotola/Lambda-PNN。