Due to the limitations of current optical and sensor technologies and the
high cost of updating them, the spectral and spatial resolution of satellites
may not always meet desired requirements. For these reasons, Remote-Sensing
Single-Image Super-Resolution (RS-SISR) techniques have gained significant
interest. In this paper, we propose Swin2-MoSE model, an enhanced version of
Swin2SR. Our model introduces MoE-SM, an enhanced Mixture-of-Experts (MoE) to
replace the Feed-Forward inside all Transformer block. MoE-SM is designed with
Smart-Merger, and new layer for merging the output of individual experts, and
with a new way to split the work between experts, defining a new per-example
strategy instead of the commonly used per-token one. Furthermore, we analyze
how positional encodings interact with each other, demonstrating that
per-channel bias and per-head bias can positively cooperate. Finally, we
propose to use a combination of Normalized-Cross-Correlation (NCC) and
Structural Similarity Index Measure (SSIM) losses, to avoid typical MSE loss
limitations. Experimental results demonstrate that Swin2-MoSE outperforms SOTA
by up to 0.377 ~ 0.958 dB (PSNR) on task of 2x, 3x and 4x resolution-upscaling
(Sen2Venus and OLI2MSI datasets). We show the efficacy of Swin2-MoSE, applying
it to a semantic segmentation task (SeasoNet dataset). Code and pretrained are
available on this https URL

我们提出了 Swin2-MoSE 模型，它是 Swin2SR 的增强版本，引入 MoE-SM（增强的专家混合编码器）来替换 Transformer 模块中的前馈网络，并通过智能合并、新的输出融合层和新的工作分配策略，改进了专家之间的合作。同时，我们分析了位置编码之间的相互作用，证明了通道偏差和头部偏差的正向合作效果。最后，我们提出采用标准化互相关（NCC）和结构相似性指数（SSIM）损失的组合，以克服常规均方损失的局限性。实验结果表明，在 2 倍、3 倍和 4 倍分辨率放大任务上，Swin2-MoSE 在 Sen2Venus 和 OLI2MSI 数据集上相对于现有最佳方法的 PSNR 提高了 0.377~0.958 dB。我们还展示了 Swin2-MoSE 在语义分割任务（SeasoNet 数据集）中的有效性。代码和预训练模型可在给出的 URL 中获取。