首頁 > 論文 > 激光與光電子學進展 > 57卷 > 2期(pp:21014--1)

基于殘差通道注意力網絡的醫學圖像超分辨率重建方法

Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network

  • 摘要
  • 論文信息
  • 參考文獻
  • 被引情況
  • PDF全文
分享:

摘要

針對醫學圖像超分辨率重建過程中高頻信息缺失導致的模糊問題,提出了一種基于殘差通道注意力網絡的醫學圖像超分辨率方法。提出的方法在殘差網絡的基本單元上去除了批規范化層以穩定訓練;去掉縮放層、添加通道注意力塊,使神經網絡更加關注含有豐富高頻信息的通道;使用亞像素卷積層進行上采樣操作得到最終輸出的高分辨率圖像。實驗結果表明,提出的方法相比主流的圖像超分辨率方法在客觀評價指標如峰值信噪比和結構相似性上有顯著提升,得到的醫學圖像紋理細節豐富,視覺體驗較好。

Abstract

To resolve the fuzzy problem caused by the lack of high-frequency information in the super-resolution reconstruction of medical images, this study proposes a medical-image super-resolution reconstruction method based on a residual channel attention network. The proposed method removes the batch normalization layer from the basic unit of the residual network (ResNet) to stabilize its training. Furthermore, it removes the scaling layer and adds a channel-attention block that focuses the ResNet on channels with abundant high-frequency details. The feature maps are subsampled using a sub-pixel convolution layer,obtaining the final high-resolution images. Experimental results show that the proposed method significantly improves objective evaluation indexes such as the peak signal-to-noise ratio and structural similarity index compared with mainstream image super-resolution methods. The obtained medical images are sufficiently detailed with high visual quality.

Newport宣傳-MKS新實驗室計劃
補充資料

中圖分類號:TP183

DOI:10.3788/LOP57.021014

所屬欄目:圖像處理

基金項目:國家重點研發計劃;

收稿日期:2019-06-04

修改稿日期:2019-07-11

網絡出版日期:2020-01-01

作者單位    點擊查看

劉可文:武漢理工大學信息工程學院, 湖北 武漢 430070武漢理工大學寬帶無線通信和傳感器網絡湖北省重點實驗室, 湖北 武漢 430070
馬圓:武漢理工大學信息工程學院, 湖北 武漢 430070武漢理工大學寬帶無線通信和傳感器網絡湖北省重點實驗室, 湖北 武漢 430070
熊紅霞:武漢理工大學土木工程與建筑學院, 湖北 武漢 430070
嚴澤軍:寧波市第一醫院泌尿外科泌尿系疾病轉化醫學研究寧波市重點實驗室, 浙江 寧波 315010
周志軍:湖北省天門市第一人民醫院泌尿外科, 湖北 天門 431700
劉朝陽:中國科學院武漢物理與數學研究所波譜與原子分子物理國家重點實驗室, 湖北 武漢 430071
房攀攀:武漢理工大學信息工程學院, 湖北 武漢 430070武漢理工大學寬帶無線通信和傳感器網絡湖北省重點實驗室, 湖北 武漢 430070
李小軍:武漢理工大學信息工程學院, 湖北 武漢 430070武漢理工大學寬帶無線通信和傳感器網絡湖北省重點實驗室, 湖北 武漢 430070
陳亞雷:武漢理工大學信息工程學院, 湖北 武漢 430070武漢理工大學寬帶無線通信和傳感器網絡湖北省重點實驗室, 湖北 武漢 430070

聯系人作者:熊紅霞(xionghongxia@whut.edu.cn)

備注:國家重點研發計劃;

【1】Park S C, Park M K, Kang M G. Super-resolution image reconstruction: a technical overview [J]. IEEE Signal Processing Magazine. 2003, 20(3): 21-36.

【2】Bai Y C, Zhang Y Q, Ding M L, et al. SOD-MTGAN: small object detection via multi-task generative adversarial network [M]. ∥Ferrari V, Hebert M, Sminchisescu C, et al. Computer vision-ECCV 2018. Lecture notes in computer science. Cham: Springer. 2018, 11217: 210-226.

【3】Mudunuri S P, Biswas S. Low resolution face recognition across variations in pose and illumination [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38(5): 1034-1040.

【4】Greenspan H. Super-resolution in medical imaging [J]. The Computer Journal. 2008, 52(1): 43-63.

【5】Chen J, Gao H B, Wang W G, et al. Methods and applications of image super-resolution restoration [J]. Laser & Optoelectronics Progress. 2015, 52(2): 020004.
陳健, 高慧斌, 王偉國, 等. 圖像超分辨率復原方法及應用 [J]. 激光與光電子學進展. 2015, 52(2): 020004.

【6】Xi Z H, Hou C Y, Yuan K P, et al. Super-resolution reconstruction of accelerated image based on deep residual network [J]. Acta Optica Sinica. 2019, 39(2): 0210003.
席志紅, 侯彩燕, 袁昆鵬, 等. 基于深層殘差網絡的加速圖像超分辨率重建 [J]. 光學學報. 2019, 39(2): 0210003.

【7】Song P F, Trzasko J D, Manduca A, et al. Improved super-resolution ultrasound microvessel imaging with spatiotemporal nonlocal means filtering and bipartite graph-based microbubble tracking [J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2018, 65(2): 149-167.

【8】Zhang Y Q, Shi F, Cheng J, et al. Longitudinally guided super-resolution of neonatal brain magnetic resonance images [J]. IEEE Transactions on Cybernetics. 2019, 49(2): 662-674.

【9】Oktay O, Ferrante E, Kamnitsas K, et al. Anatomically constrained neural networks (ACNNs): application to cardiac image enhancement and segmentation [J]. IEEE Transactions on Medical Imaging. 2018, 37(2): 384-395.

【10】Dencks S, Piepenbrock M, Opacic T, et al. Clinical pilot application of super-resolution US imaging in breast cancer [J]. IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control. 2019, 66(3): 517-526.

【11】Chu J H, Hu F S, Zhang J Q, et al. An improved single-frame super-resolution algorithm for magnetic resonance image [J]. Laser & Optoelectronics Progress. 2018, 55(5): 051009.
褚晶輝, 胡風碩, 張佳祺, 等. 一種改進的單幀磁共振圖像超分辨率算法 [J]. 激光與光電子學進展. 2018, 55(5): 051009.

【12】Dai S Y, Han M, Xu W, et al. SoftCuts: a soft edge smoothness prior for color image super-resolution [J]. IEEE Transactions on Image Processing. 2009, 18(5): 969-981.

【13】Purkait P, Chanda B. Super resolution image reconstruction through Bregman iteration using morphologic regularization [J]. IEEE Transactions on Image Processing. 2012, 21(9): 4029-4039.

【14】Yang J C, Wright J, Huang T S, et al. Image super-resolution via sparse representation [J]. IEEE Transactions on Image Processing. 2010, 19(11): 2861-2873.

【15】Dong C, Loy C C, He K M, et al. Image super-resolution using deep convolutional networks [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2016, 38(2): 295-307.

【16】Kim J, Lee J K, Lee K M. Accurate image super-resolution using very deep convolutional networks . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE. 2016, 1646-1654.

【17】Shi W Z, Caballero J, Huszár F, et al. Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE. 2016, 1874-1883.

【18】Dong C, Loy C C, Tang X O. Accelerating the super-resolution convolutional neural network [M]. ∥Leibe B, Matas J, Sebe N, et al. Computer vision-ECCV 2016. Lecture notes in computer science. Cham: Springer. 2016, 9906: 391-407.

【19】He K M, Zhang X Y, Ren S Q, et al. Deep residual learning for image recognition . [C]∥2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 27-30, 2016, Las Vegas, NV, USA. New York: IEEE. 2016, 770-778.

【20】Ledig C, Theis L, Huszar F, et al. Photo-realistic single image super-resolution using a generative adversarial network . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 105-114.

【21】Lim B, Son S, Kim H, et al. Enhanced deep residual networks for single image super-resolution . [C]∥2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), July 21-26, 2017, Honolulu, HI, USA. New York: IEEE. 2017, 1132-1140.

【22】Hu J, Shen L, Sun G. Squeeze-and-excitation networks . [C]∥Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, June 18-22, 2018, Salt Lake City, Utah. New York: IEEE. 2018, 7132-7141.

【23】Clevert D A, Unterthiner T, Hochreiter S. Fast. -02-22)[2019-06-03] . https:∥arxiv. 2016, org/abs/1511: 07289.

【24】Hiai F, Lin M. On an eigenvalue inequality involving the Hadamard product [J]. Linear Algebra and its Applications. 2017, 515: 313-320.

【25】Zhao H, Gallo O, Frosio I, et al. Loss functions for image restoration with neural networks [J]. IEEE Transactions on Computational Imaging. 2017, 3(1): 47-57.

【26】Kingma D P. -01-30)[2019-06-03] . https:∥arxiv. 2017, org/abs/1412: 6980.

【27】Data science bowl 2017[EB/OL]. bowl 2017[EB/OL] [2019-06-03]. https:∥www.kaggle.com/c/data-science-bowl-. 2017.

【28】L Geert, D Oscar, B Jelle, et al. 2019-06-03) . 2017.

【29】Pomi A, Slusallek P. Interactive ray tracing for virtual TV studio applications [J]. JVRB-Journal of Virtual Reality and Broadcasting. 2005, 2(1): 1-10.

引用該論文

Liu Kewen,Ma Yuan,Xiong Hongxia,Yan Zejun,Zhou Zhijun,Liu Chaoyang,Fang Panpan,Li Xiaojun,Chen Yalei. Medical-Image Super-Resolution Reconstruction Method Based on Residual Channel Attention Network[J]. Laser & Optoelectronics Progress, 2020, 57(2): 021014

劉可文,馬圓,熊紅霞,嚴澤軍,周志軍,劉朝陽,房攀攀,李小軍,陳亞雷. 基于殘差通道注意力網絡的醫學圖像超分辨率重建方法[J]. 激光與光電子學進展, 2020, 57(2): 021014

您的瀏覽器不支持PDF插件,請使用最新的(Chrome/Fire Fox等)瀏覽器.或者您還可以點擊此處下載該論文PDF

热博rb88 <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <蜘蛛词>| <文本链> <文本链> <文本链> <文本链> <文本链> <文本链>