Yu Yue, She Kun, Liu Jinhua
School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China.
School of Mathematical and Computer Sciences, Shangrao Normal University, Shangrao 334001, China.
Micromachines (Basel). 2021 Nov 18;12(11):1418. doi: 10.3390/mi12111418.
Medical imaging is widely used in medical diagnosis. The low-resolution image caused by high hardware cost and poor imaging technology leads to the loss of relevant features and even fine texture. Obtaining high-quality medical images plays an important role in disease diagnosis. A surge of deep learning approaches has recently demonstrated high-quality reconstruction for medical image super-resolution. In this work, we propose a light-weight wavelet frequency separation attention network for medical image super-resolution (WFSAN). WFSAN is designed with separated-path for wavelet sub-bands to predict the wavelet coefficients, considering that image data characteristics are different in the wavelet domain and spatial domain. In addition, different activation functions are selected to fit the coefficients. Inputs comprise approximate sub-bands and detail sub-bands of low-resolution wavelet coefficients. In the separated-path network, detail sub-bands, which have more sparsity, are trained to enhance high frequency information. An attention extension ghost block is designed to generate the features more efficiently. All results obtained from fusing layers are contracted to reconstruct the approximate and detail wavelet coefficients of the high-resolution image. In the end, the super-resolution results are generated by inverse wavelet transform. Experimental results show that WFSAN has competitive performance against state-of-the-art lightweight medical imaging methods in terms of quality and quantitative metrics.
医学成像在医学诊断中被广泛应用。由于硬件成本高和成像技术不佳导致的低分辨率图像会造成相关特征甚至精细纹理的丢失。获取高质量的医学图像在疾病诊断中起着重要作用。最近，大量深度学习方法已证明可用于医学图像超分辨率的高质量重建。在这项工作中，我们提出了一种用于医学图像超分辨率的轻量级小波频率分离注意力网络（WFSAN）。考虑到图像数据特征在小波域和空间域有所不同，WFSAN设计了小波子带的分离路径来预测小波系数。此外，选择不同的激活函数以拟合系数。输入包括低分辨率小波系数的近似子带和细节子带。在分离路径网络中，对具有更多稀疏性的细节子带进行训练以增强高频信息。设计了一个注意力扩展幽灵块以更高效地生成特征。从融合层获得的所有结果被收缩以重建高分辨率图像的近似和细节小波系数。最后，通过小波逆变换生成超分辨率结果。实验结果表明，在质量和定量指标方面，WFSAN与最先进的轻量级医学成像方法相比具有竞争力。
