Super-Resolution Reconstruction of Speckle Images of Engineered Bamboo Based on an Attention-Dense Residual Network

Wei Yu; Zheng Liu; Zilong Zhuang; Ying Liu; Xu Wang; Yutu Yang; Binli Gou

doi:10.3390/s22176693

Super-Resolution Reconstruction of Speckle Images of Engineered Bamboo Based on an Attention-Dense Residual Network

Sensors (Basel). 2022 Sep 4;22(17):6693. doi: 10.3390/s22176693.

Authors

Wei Yu¹, Zheng Liu¹, Zilong Zhuang¹, Ying Liu¹, Xu Wang¹, Yutu Yang¹, Binli Gou¹

Affiliation

¹ Jiangsu Co-Innovation Center of Efficient Processing and Utilization of Forest Resources, College of Mechanical and Electronic Engineering, Nanjing Forestry University, Nanjing 210037, China.

Abstract

With the global population surge, the consumption of nonrenewable resources and pollution emissions have reached an alarming level. Engineered bamboo is widely used in construction, mechanical and electrical product packaging, and other industries. Its main damage is the material fracture caused by the expansion of initial cracks. In order to accurately detect the length of crack propagation, digital image correlation technology can be used for calculation. At present, the traditional interpolation method is still used in the reconstruction of engineered bamboo speckle images for digital correlation technology, and the performance is relatively lagging. Therefore, this paper proposes a super-resolution reconstruction method of engineering-bamboo speckle images based on an attention-dense residual network. In this study, the residual network is improved by removing the BN layer, using the L1 loss function, introducing the attention model, and designing an attention-intensive residual block. An image super-resolution model based on the attention-dense residual network is proposed. Finally, the objective evaluation indexes PSNR and SSIM and subjective evaluation index MOS were used to evaluate the performance of the model. The ADRN method was 29.19 dB, 0.938, and 3.19 points in PSNR, SSIM, and MOS values. Compared to the traditional BICUBIC B-spline interpolation method, the speckle images reconstructed by this model increased by 8.55 dB, 0.323, and 1.43 points, respectively. Compared to the SRResNet method, the speckle images reconstructed by this model were increased by 4.53 dB, 0.111, and 0.14 points, respectively. The reconstructed speckle images of engineered bamboo were clearer, and the image features were more obvious, which could better identify the tip crack position of the engineered bamboo. The results show that the super-resolution reconstruction effect of engineered-bamboo speckle images can be effectively improved by adding the attention mechanism to the residual network. This method has great application value.

Keywords: attention-dense residual network; engineered bamboo; speckle images; super-resolution.

Grants and funding

This research was funded by the Postgraduate Research & Practice Innovation Program of Jiangsu Province under grant KYCX22_1050 and the 2019 Jiangsu Province Key Research and Development Plan by the Jiangsu Province Science and Technology under grant BE2019112. This research was also funded by the National Natural Science Foundation of China under Grant 61901221, in part by the Postgraduate Research and Practice Innovation Program of Jiangsu Province under Grant KYCX21_0872, and in part by the National Key Research and Development Program of China under Grant 2019YFD1100404.