To obtain remote sensing images with more high-frequency information and textural detail information and solve the problems of super-resolution networks, such as complex structure, numerous parameters and large model size, this paper proposes a multiscale parameter-free attention mechanism enhanced network. First, the proposed network uses convolutional layers to extract shallow features from low-resolution remote sensing images. The shallow features are then input to the proposed multiscale parameter-free attention enhancement network, which combines parallel connection of multiple convolutional layers with different-sized convolutional kernels to refine the extraction of multiscale features. The proposed network also enhances feature information with a high contribution via the symmetric activation function to inhibit redundant information under the parameter-free attention mechanism. After six residual-connected multiscale parameter-free attention enhancement modules, the reconstruction module generates the final reconstructed image. Experimental results demonstrate that compared with the existing representative methods, the proposed network exhibits significant reconstruction advantages in terms of performance metrics and visual effects. Moreover, the peak signal-to-noise ratio and structural similarity of the proposed network outperformed those of the compared methods.