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郑跃坤, 葛明锋, 常智敏, 董文飞. 365赌球注册开户[J]. 365赌球, 2023, 16(5): 1022-1033. doi: 10.37188/CO.2022-0247
引用本文: 郑跃坤, 葛明锋, 常智敏, 董文飞. 365赌球注册开户[J]. 365赌球, 2023, 16(5): 1022-1033. doi: 10.37188/CO.2022-0247
ZHENG Yue-kun, GE Ming-feng, CHANG Zhi-min, DONG Wen-fei. 365赌球[J]. Chinese Optics, 2023, 16(5): 1022-1033. doi: 10.37188/CO.2022-0247
Citation: ZHENG Yue-kun, GE Ming-feng, CHANG Zhi-min, DONG Wen-fei. 365赌球[J]. Chinese Optics, 2023, 16(5): 1022-1033. doi: 10.37188/CO.2022-0247

365赌球注册开户

doi: 10.37188/CO.2022-0247
基金项目:国家重点研发计划(No. 2021YFB3602200);苏州市科技计划项目(No. SZS201903)
详细信息
    作者简介:

    郑跃坤(1998—),男,广东汕头人,硕士研究生,2020年于中山大学获得学士学位,主要从事图像处理、深度学习等方面的研究。E-mail:[email protected]

    葛明锋(1987—),男,江苏南通人,博士,副研究员,硕士生导师,主要从事高光谱、荧光显微成像方面研究。E-mail:[email protected]

    董文飞(1975—),男,吉林长春人,博士,研究员,博士生导师,1999年于中国科学院长春应用化学研究所获得高分子物理化学专业硕士学位,2004年于德国马普胶体界面所和波兹坦大学获得自然科学博士学位,主要从事纳米材料和技术在生物医用光子学领域的应用基础研究。E-mail: [email protected]

  • 中图分类号:TP391.41

365赌球

Funds:Supported by the National Key R & D Program of China (No. 2021YFB3602200); Suzhou Science and Technology Plan Project (No.SZS201903)
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  • 摘要:

    针对结直肠镜图像分辨率偏低、纹理信息偏少和细节模糊等缺点,提出了一种基于残差注意力网络的图像超分辨率重建算法SMRAN,选取结直肠息肉内窥镜图像数据集PolypsSet中的部分图像作为原始数据进行实验。首先,使用卷积网络提取低分辨率图像的浅层特征;其次,设计Res-Sobel结构对图像边缘特征进行增强;然后,通过引入不同大小的卷积核,设计多尺度特征融合模块(Multi-Scale feature Extraction Block, MEB),自适应地提取不同尺度的特征,从而得到有效的图像信息,并通过残差注意力网络将Res-Sobel模块和多尺度特征融合模块MEB进行连接;最后,通过亚像素卷积层对图像进行重建,得到最终的高分辨率图像。在尺度因子为×4时,网络在测试集上的测试结果如下: 峰值信噪比PSNR为34.25 dB,结构相似性SSIM为0.8675。实验结果表明,与传统的双三次插值算法及常用的SRCNN、RCAN等深度学习算法相比,本文提出的SMRAN对结直肠内窥镜图像具有更好的超分辨率重建效果。

  • 图 1 SMRAN架构

    Figure 1. Architecture of SMRAN

    图 2 Res-Sobel模块

    Figure 2. Res-Sobel Block

    图 3 多尺度特征融合模块(MEB)

    Figure 3. Multi-scale feature extraction block(MEB)

    图 4 CBAM注意力机制

    Figure 4. CBAM attention mechanism

    图 5 SMRAB结构

    Figure 5. SMRAB Structure

    图 6 验证集的PSNR曲线

    Figure 6. PSNR curve of validation set

    图 7 验证集的SSIM曲线

    Figure 7. SSIM curve of validation set

    图 8 采用不同超分辨率算法的结直肠息肉内窥图像的重建效果对比图

    Figure 8. Comparison of reconstruction effects of endoscopic images of colorectal polyps using different super-resolution algorithms

    图 9 SMRAN模型对光学分辨率的提升效果

    Figure 9. Improvement of optical resolution by SMRAN model

    表  1 测试集上不同算法的PSNR值

    Table  1. PSNR values of different algorithms on the testing set (Unit: dB)

    算法 PSNR(dB)
    ×2 ×3 ×4
    Bicubic 33.85 31.94 29.91
    SRCNN 36.70 34.53 32.04
    FSRCNN 37.63 35.22 32.23
    EDSR 37.34 35.25 32.13
    ESPCN 36.75 34.78 31.38
    RCAN 39.04 35.63 33.86
    本文算法 39.69 36.92 34.25
    下载: 导出CSV

    表  2 测试集上不同算法的SSIM值

    Table  2. SSIM values of different algorithms on the testing set

    算法 SSIM
    ×2 ×3 ×4
    Bicubic 0.9121 0.8824 0.8103
    SRCNN 0.9400 0.8983 0.8642
    FSRCNN 0.9382 0.9132 0.8660
    EDSR 0.9325 0.9158 0.8401
    ESPCN 0.9392 0.9003 0.8566
    RCAN 0.9483 0.9182 0.8667
    本文算法 0.9559 0.9249 0.8675
    下载: 导出CSV

    表  3 不同损失函数的PSNR和SSIM值

    Table  3. PSNR and SSIM values for different loss functions

    损失函数 PSNR(dB) SSIM
    ${L_1}$ 34.27 0.8664
    ${L_1}$+MS_SSIM 34.25 0.8675
    下载: 导出CSV

    表  4 各模块对性能的影响

    Table  4. The impact of each module on performance

    Res-Sobel Block MEB CBAM PSNR(dB)/SSIM
    33.33/0.8577
    33.39/0.8601
    33.47/0.8609
    34.25/0.8675
    下载: 导出CSV

    表  5 Kvasir-SEG数据集上不同算法的PSNR值

    Table  5. PSNR values of different algorithms on the Kvasir-SEG dataset (Unit: dB)

    算法 PSNR(dB)
    ×2 ×3 ×4
    Bicubic 37.35 33.86 31.78
    SRCNN 39.98 35.98 33.23
    FSRCNN 40.62 36.57 33.68
    EDSR 40.94 36.69 33.99
    ESPCN 39.71 35.91 33.01
    RCAN 41.58 37.62 34.23
    本文算法 41.80 37.81 34.56
    下载: 导出CSV

    表  6 Kvasir-SEG数据集上不同算法的SSIM值

    Table  6. SSIM values of different algorithms on the Kvasir-SEG dataset

    算法 SSIM
    ×2 ×3 ×4
    Bicubic 0.9776 0.9469 0.9079
    SRCNN 0.9833 0.9658 0.9242
    FSRCNN 0.9859 0.9672 0.9256
    EDSR 0.9867 0.9669 0.9219
    ESPCN 0.9886 0.9710 0.9396
    RCAN 0.9859 0.9702 0.9447
    本文算法 0.9884 0.9714 0.9456
    下载: 导出CSV
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  • 收稿日期: 2022-11-29
  • 录用日期: 2023-03-15
  • 修回日期: 2022-12-23
  • 网络出版日期: 2023-04-04

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