Mr. Yanbin Peng Profile

Yanbin Peng

at Peking Univ Shenzhen Hospital

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Publications (2)

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Proceedings Article | 15 February 2021 Presentation + Paper

Single MR image super-resolution via mixed self-similarity attention network

Xiaowan Hu, Haoqian Wang, Yi Luo, Zhongzhi Sun, Yanbin Peng

Proceedings Volume 11596, 1159605 (2021) https://doi.org/10.1117/12.2580860

KEYWORDS: Magnetic resonance imaging, Super resolution, Medical imaging, Visualization, Magnetism, Lawrencium, Image restoration

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The single-image super-resolution (SISR) network based on deep learning is dedicated to learning the mapping between low-resolution (LR) images and high-resolution (HR) images. The optimal parameters of these networks often require extensive training on large-scale external image databases. For medical magnetic resonance (MR) images, there is a lack of large data sets containing high-quality images. Some deep networks that perform well on natural images cannot be fully trained on MR images, which limits the super-resolution (SR) performance. In traditional methods, the non-local self-similarity has been verified as useful statistical prior information for image restoration. The inherent feature correlation not only exists between pixels, but some patches also tend to be repeated at different positions within and across scales of MR images. Therefore, in this paper, we propose a mixed self-similarity attention network (MSAN) to explore the long-range dependencies of different regions fully. In the feature map of the entire input MR image, the prior information of self-similarity is divided into two scales: point-similarity and patch-similarity. We use points and patches that are highly similar to the current area to restore a more detailed structural texture. The internal correlation items can be used as an essential supplement to the limited external training dataset. Besides, the large number of less informative background in MR images will interfere with practical self-similarity information. A dual attention mechanism combining first-order attention and second-order attention gives more weight to salient features and suppresses the activation of useless features. Comprehensive experiments demonstrate that the proposed achieves significantly superior results on MR images SR while outperforming state-of-the-art methods by a large margin quantitatively and visually.

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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