Pansharpening is a crucial technique in remote sensing for enhancing spatial resolution by fusing low spatial resolution multispectral (LRMS) images with high spatial panchromatic (PAN) images. Existing deep convolutional networks often face challenges in capturing fine details due to the homogeneous operation of convolutional kernels. In this paper, we propose a novel predictive filtering approach for pansharpening to mitigate spectral distortions and spatial degradations. By obtaining predictive filters through the fusion of LRMS and PAN and conducting filtering operations using unique kernels assigned to each pixel, our method reduces information loss significantly. To learn more effective kernels, we propose an effective fine-grained fusion method for LRMS and PAN features, namely element-wise feature mixing. Specifically, features of LRMS and PAN will be exchanged under the guidance of a learned mask. The value of the mask signifies the extent to which the element will be mixed. Extensive experimental results demonstrate that the proposed method achieves better performances than the state-of-the-art models with fewer parameters and lower computations. Visual comparisons indicate that our model pays more attention to details, which further confirms the effectiveness of the proposed fine-grained fusion method. Codes are available at https://github.com/yc-cui/PreMix.

中文翻译：

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通过预测过滤和元素级特征混合进行全色锐化


全色锐化是遥感中一种重要的技术，通过将低空间分辨率多光谱 （LRMS） 图像与高空间全色 （PAN） 图像融合来提高空间分辨率。由于卷积核的同质操作，现有的深度卷积网络在捕获精细细节方面经常面临挑战。在本文中，我们提出了一种新的全色锐化预测过滤方法，以减轻频谱失真和空间退化。通过融合 LRMS 和 PAN 获得预测滤波器，并使用分配给每个像素的唯一内核进行过滤操作，我们的方法显着减少了信息损失。为了学习更有效的内核，我们提出了一种有效的 LRMS 和 PAN 特征细粒度融合方法，即元素级特征混合。具体来说，LRMS 和 PAN 的特征将在学习面具的指导下进行交换。掩码的值表示元素将混合的程度。大量的实验结果表明，所提出的方法比参数更少、计算量更少的最先进模型取得了更好的性能。视觉比较表明，我们的模型更注重细节，这进一步证实了所提出的细粒度融合方法的有效性。代码可在 https://github.com/yc-cui/PreMix 处获得。