将理想高分辨率多光谱图像与遥感融合结果之间的残差视为广义噪声,提出了基于深度残差去噪网络(DnCNN)的遥感融合图像质量提升算法。通过DnCNN学习固定融合算法中细节丢失或光谱扭曲的规律,将输入的遥感图像融合结果映射得到残差图像,再用残差图像补充和修复遥感融合结果。在Quickbird卫星遥感图像数据上,利用本文算法对不同方法的融合结果进行增强处理测试,实验结果表明所有算法结果经过DnCNN的后置增强之后,融合质量都大为改善,其中基于支持值变换(SVT)的方法与DnCNN结合的算法性能最好,其性能优于现有最新的遥感图像融合方法。

We considered the residual between an ideal high spatial resolution multi-spectral image and a pansharpened image as generalized noise, and thus proposed a deep residual denoising network (DnCNN)-based quality boosting method for the pansharpened image. We used the DnCNN to learn the patterns of detail loss and spectral distortion of the fixed fusion algorithm, and mapped the input pansharpened image to a residual image. Then, we used the residual image to compensate and repair the pansharpened image. In an experiment using the QuickBird dataset, images pansharpened using different methods were enhanced via the proposed method. The experimental results demonstrate that, using the proposed method, the qualities of all pansharpened images are improved and the best boosting is attained when this method is used in conjunction with the support value transform based method. The proposed method outperforms latest methods.