Super-resolution reconstruction for colorectal endoscopic images based on a residual network
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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。
Abstract:In this paper, an image super-resolution reconstruction multi-scale algorithm based on a residual attention network (SMRAN) is proposed to solve the problems caused by low resolutions, less texture information and blurred details in colorectal endoscopic images. Images from the colorectal polyp endoscope image dataset PolypsSet are selected as the raw data for these experiments. A convolutional network is built to extract the shallow features of the low-resolution image and a Res-Sobel block is designed to enhance its edge features. A multi-scale feature fusion block MEB is designed by introducing convolution kernels of different sizes to adaptively extract image features of different scales and obtain effective image information. The Res-Sobel block and multi-scale feature fusion module block MEB are connected through the residual attention network. Finally, a high-resolution image is reconstructed at the sub-pixel convolution layer. When the amplification factor is ×4, the performance of the proposed algorithm on the test set are as follows: the peak signal-to-noise ratio (PSNR) is 34.25 dB and the structural similarity (SSIM) is 0.8675. Compared with the traditional bicubic interpolation algorithm and commonly used deep learning algorithms such as SRCNN and RCAN, the proposed SMRAN algorithm shows better super-resolution reconstruction results on colorectal endoscopic images.
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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
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 
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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
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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
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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
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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
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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
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