A self-adapting super-resolution structures framework for automatic design of GAN

by   Yibo Guo, et al.

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.



There are no comments yet.


page 4

page 9


Super-Resolution Image Reconstruction Based on Self-Calibrated Convolutional GAN

With the effective application of deep learning in computer vision, brea...

Novel Super-Resolution Method Based on High Order Nonlocal-Means

Super-resolution without explicit sub-pixel motion estimation is a very ...

Deep learning-based super-resolution in coherent imaging systems

We present a deep learning framework based on a generative adversarial n...

Fine-grained Attention and Feature-sharing Generative Adversarial Networks for Single Image Super-Resolution

The traditional super-resolution methods that aim to minimize the mean s...

Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization

Image demosaicing and super-resolution are two important tasks in color ...

BSRA: Block-based Super Resolution Accelerator with Hardware Efficient Pixel Attention

Increasingly, convolution neural network (CNN) based super resolution mo...

Remote sensing image fusion based on Bayesian GAN

Remote sensing image fusion technology (pan-sharpening) is an important ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.