Mr. Yuanhao Cai Profile

Yuanhao Cai

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Proceedings Article | 10 October 2020 Presentation + Paper

EG^2N: enhanced gradient guiding network for single MR image super-resolution

Xiaowan Hu, Yuanhao Cai, Haoqian Wang, Yanbin Peng, Yulun Zhang

Proceedings Volume 11550, 115500I (2020) https://doi.org/10.1117/12.2575261

KEYWORDS: Image enhancement, Magnetic resonance imaging, Super resolution, Medical imaging, Magnetism, Tissues, Gallium nitride, Image restoration, Wavelets, Reconstruction algorithms

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During magnetic resonance imaging (MRI), the strong response to the signal is usually displayed as structural edges and textures, which is important for distinguishing different tissues and lesions. In the current superresolution (SR) methods with the usage of deep learning, some low-level structural information tends to gradually disappear as the network deepens, resulting in excessive smoothness in high-frequency regions. This phenomenon is particularly noticeable in MRI with poor brightness contrast and small gray dynamic range. Although the generative adversarial network (GAN) can repair structured textures well in natural images, it is likely to learn patterns that do not exist in the images, which poses risks to the reconstruction of medical images. Therefore, we propose an enhanced gradient guiding network (EG²N) to alleviate these problems. On the one hand, for improving the contrast and suppress the noise effectively, we use a multi-scale wavelet enhancement for preprocessing, where the enhanced gradient map is considered as the structural prior. On the other hand, blindly using dense connections in the feed-forward network will bring about redundancy, so structural features from an additional branch are added to specific layers as a supplement to high-level features and constrain optimization. We add a feedback mechanism to promote cross-layer flow between low-level and high-level features. In addition, the perceptual loss is added to avoid distortion caused by excessive smoothing. The experimental results show that our method achieves the best visual results and excellent performance compared with state-of-the-art methods on most popular MR images SR benchmarks.

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