  Image generation from a single image using generative adversarial networks is
quite interesting due to the realism of generated images. However, recent
approaches need improvement for such realistic and diverse image generation,
when the global context of the image is important such as in face, animal, and
architectural image generation. This is mainly due to the use of fewer
convolutional layers for mainly capturing the patch statistics and, thereby,
not being able to capture global statistics very well. We solve this problem by
using attention blocks at selected scales and feeding a random Gaussian blurred
image to the discriminator for training. Our results are visually better than
the state-of-the-art particularly in generating images that require global
context. The diversity of our image generation, measured using the average
standard deviation of pixels, is also better.
