Generative adversarial networks (GANs) are utilized for synthetic aperture radar (SAR) image super-resolution reconstruction, affording realistic texture details. However, existing GANs only discriminate the final generated high-resolution (HR) image after two consecutive upsampling processes, which ignore some high-frequency information of the reconstructed images. To resolve this issue, a multiresolution fusion discrimination (MRFD) algorithm is proposed to discriminate the reconstructed feature maps after each upsampling. First, a multiresolution discrimination process discriminates the authenticity of each upsampled feature map separately, which reduces the image distortion imposed during two consecutive upsampling processes. Besides, multiresolution feature fusion further preserves the consistent high-frequency texture structures. Finally, a multiscale dense network extracts image features in different scales, with multiscale dense block’s dense connections improving parameter utilization. The experimental results on a SAR dataset demonstrate that the proposed MRFD algorithm performs better in reconstructing the texture details of HR images.