Multi-Scale Mixed Pixel Network for Remote Sensing Image Super-Resolution

Deep learning-based image super-resolution (SR) technology has gained extensive attention in the remote sensing community, which aims to reconstruct the abundant details of target images. However, the practical application of existing deep learning-based SR methods is often limited by the trade-off between network performance and computational load. This paper presents a novel multi-scale mixed pixel network (MSMP-Net) to explore the precise and cost-effective SR reconstruction of remote sensing images. Specifically, a dual-path feature capture mechanism (DPFCM) architecture is devised that incorporates diverse dilated convolution operations to comprehensively capture hierarchical features across varying receptive fields. In addition, a triple-path high-resolution rebuilder (TPHRR) structure is designed, which uses a set of dense branches, residual branches and pyramid branches to effectively promote the transfer and reuse of features. Extensive experiments reveal that our proposed MSMP-Net outperforms other state-of-the-art SR methods in reconstruction effects.