Scale-Frequency Dual-Modulation Method for Remote Sensing Image Continuous Super-Resolution

In recent years, the development of continuous-scale super-resolution (SR) methods in the field of remote sensing (RS) has garnered significant attention. These innovative methods are capable of delivering arbitrary-scale image SR through a single unified network. However, the majority of these methods employ the same feature extractor for different SR scales, which constrains the enhancement of network performance. Furthermore, the utilization of a multilayer perceptron for image reconstruction results in the loss of a substantial quantity of high-frequency information, which is of particular significance in the context of RS images. This, in turn, gives rise to the generation of blurred SR results. In order to address the aforementioned issues, the scale-frequency dual-modulation network (SFMNet) is proposed as a means of achieving RS image continuous SR. First, scale modulation feature fusion can modulate different levels of feature fusion according to different scale factors, thereby fully integrating the scale information into the feature extraction process of the network. Subsequently, frequency modulation reconstruction can modulate the frequency-domain information at the root of the image reconstruction process, thereby enhancing the ability of the network to learn high-frequency information. The experimental results demonstrate that the proposed SFMNet outperforms existing RS image continuous SR methods in terms of quantitative indices and visual quality.