  With the effective application of deep learning in computer vision,
breakthroughs have been made in the research of super-resolution images
reconstruction. However, many researches have pointed out that the
insufficiency of the neural network extraction on image features may bring the
deteriorating of newly reconstructed image. On the other hand, the generated
pictures are sometimes too artificial because of over-smoothing. In order to
solve the above problems, we propose a novel self-calibrated convolutional
generative adversarial networks. The generator consists of feature extraction
and image reconstruction. Feature extraction uses self-calibrated convolutions,
which contains four portions, and each portion has specific functions. It can
not only expand the range of receptive fields, but also obtain long-range
spatial and inter-channel dependencies. Then image reconstruction is performed,
and finally a super-resolution image is reconstructed. We have conducted
thorough experiments on different datasets including set5, set14 and BSD100
under the SSIM evaluation method. The experimental results prove the
effectiveness of the proposed network.
