PSNR and SSIM comparison results among different methods.

Boldface indicates the best performance and italics indicate the second-best performance.</p

Architecture of DCU.

It mainly contains encoder and decoder layers, and we can use different kernel sizes to achieve different scales of information.</p

The comparative results of Test20 dataset magnified by an up-scaling factor 3.

The comparative results of Test20 dataset magnified by an up-scaling factor 3.</p

The visual comparison results magnified by an upscaling factor 4.

The visual comparison results magnified by an upscaling factor 4.</p

The PSNR and SSIM of the different number of stacked DCUs.

The PSNR and SSIM of the different number of stacked DCUs.</p

Comparison between model complexity and image quality.

The left vertical axis is the number of parameters, and the right vertical axis is the size of the model file.</p

PSNR/SSIM comparison on remote sensing test datasets among different methods with up-scaling factor ×4.

Boldface indicates the best performance and italics indicate the second-best performance.</p

The visual comparison of Test20 dataset SR obtained using different methods with an up-scaling factor 4.

The visual comparison of Test20 dataset SR obtained using different methods with an up-scaling factor 4.</p

PSNR comparison of different kernel sizes for the MCSCM.

PSNR comparison of different kernel sizes for the MCSCM.</p

Architecture of the proposed MCSCN model.

Our network consists of three main parts: BFE, MCSCM, and reconstruction module. We introduce a MCSCM to complement the information and make full use of the different scales of feature information.</p