In super-resolution magnetic resonance imaging (SR-MRI), the low-resolution scans are acquired keeping the central low-frequency part of the Fourier space intact, and filling the unacquired portion with zeroes. While transforming the zero-padded low-resolution acquired k-space to the image domain through inverse Fourier transformation, an inherent blur and high-frequency oscillatory artifacts are manifested in the reconstructed image. The inherent limitation of blur and ringing present in some of the subtle clinical features of images reconstructed by existing deep-learning-based approaches are overcome by the proposed interleaved hybrid domain convolutional neural network (CNN) model consisting of a Fourier and spatial domain convolution layers.