Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6864235 | Neurocomputing | 2018 | 27 Pages |
Abstract
Convolutional neural networks based models have made impressive advances for single-image super-resolution task. To advance the reconstruction quality of high-frequency details of the images, which are difficult to recover in super-resolution task, this paper proposes a super-resolution method using a high-frequency information enhancing densely connected convolutional neural network (SRDN) which can make the network pay more attention to high-frequency regions' reconstruction like edges and textures during training. Our method applies relatively higher weights on the gradient descent values of these high-frequency regions' pixels before they are propagated backward to update the parameters of the network during training. After that, we use a Generative Adversarial Network to finetune the trained model for finer texture details and more photo-realistic results. Experiments show that our approach can achieve a significant boost in the reconstruction quality of high-frequency details at high magnification ratios. We also design a novel measurement to evaluate the high-frequency details' difference (HFD) between the ground truth image and the generated image.
Keywords
Related Topics
Physical Sciences and Engineering
Computer Science
Artificial Intelligence
Authors
Fuqiang Zhou, Xiaojie Li, Zuoxin Li,