  With the development of deep learning, the single super-resolution image
reconstruction network models are becoming more and more complex. Small changes
in hyperparameters of the models have a greater impact on model performance. In
the existing works, experts have gradually explored a set of optimal model
parameters based on empirical values or performing brute-force search. In this
paper, we introduce a new super-resolution image reconstruction generative
adversarial network framework, and a Bayesian optimization method used to
optimizing the hyperparameters of the generator and discriminator. The
generator is made by self-calibrated convolution, and discriminator is made by
convolution lays. We have defined the hyperparameters such as the number of
network layers and the number of neurons. Our method adopts Bayesian
optimization as a optimization policy of GAN in our model. Not only can find
the optimal hyperparameter solution automatically, but also can construct a
super-resolution image reconstruction network, reducing the manual workload.
Experiments show that Bayesian optimization can search the optimal solution
earlier than the other two optimization algorithms.
