Enhancement of SAR resolution is essential for various applications in earth observation. Since SAR images are highly corrupted by speckle noise, we propose to help super-resolution neural network learning with a despeckling preprocessing step. Unlike optical images, low-resolution SAR images are extracted from the sub-apertures of the original SAR image. To evaluate the impact of the despeckling, SwinIR, SRCNN, and ESPCN neural networks are trained in three ways: Noisy2Noisy, Noisy2Denoised, and Denoised2Denoised. The ONERA SAR database experiments show the despeckling improvement gap and the slight enhancement of SwinIR over SRCNN and ESPCN according to the visual reconstruction and to L 1 , L 2 , PSNR, and SSIM metrics