人工智能
计算机科学
卷积神经网络
残余物
深度学习
计算机视觉
超分辨率
图像分辨率
模式识别(心理学)
分辨率(逻辑)
图像(数学)
高分辨率
算法
遥感
地质学
作者
Yutaro Iwamoto,Kyohei Takeda,Yinhao Li,Akihiko Shiino,Yen Wei Chen
出处
期刊:IEEE transactions on emerging topics in computational intelligence
[Institute of Electrical and Electronics Engineers]
日期:2023-04-01
卷期号:7 (2): 426-435
被引量:2
标识
DOI:10.1109/tetci.2022.3215137
摘要
Deep learning techniques have led to state-of-the-art image super resolution with natural images. Normally, pairs of high-resolution and low-resolution images are used to train the deep learning models. These techniques have also been applied to medical image super-resolution. The characteristics of medical images differ significantly from natural images in several ways. First, it is difficult to obtain high-resolution images for training in real clinical applications due to the limitations of imaging systems and clinical requirements. Second, other modal high-resolution images are available (e.g., high-resolution T1-weighted images are available for enhancing low-resolution T2-weighted images). In this paper, we propose an unsupervised image super-resolution technique based on simple prior knowledge of the human anatomy. This technique does not require target T2WI high-resolution images for training. Furthermore, we present a guided residual dense network, which incorporates a residual dense network with a guided deep convolutional neural network for enhancing the resolution of low-resolution images by referring to different modal high-resolution images of the same subject. Experiments on a publicly available brain MRI database showed that our proposed method achieves better performance than the state-of-the-art methods.
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