Yu, W.; Zhou, H.; Liu, Y.; Yang, Y.; Shen, Y. Super-Resolution Reconstruction of Particleboard Images Based on Improved SRGAN. Forests2023, 14, 1842.
Yu, W.; Zhou, H.; Liu, Y.; Yang, Y.; Shen, Y. Super-Resolution Reconstruction of Particleboard Images Based on Improved SRGAN. Forests 2023, 14, 1842.
Yu, W.; Zhou, H.; Liu, Y.; Yang, Y.; Shen, Y. Super-Resolution Reconstruction of Particleboard Images Based on Improved SRGAN. Forests2023, 14, 1842.
Yu, W.; Zhou, H.; Liu, Y.; Yang, Y.; Shen, Y. Super-Resolution Reconstruction of Particleboard Images Based on Improved SRGAN. Forests 2023, 14, 1842.
Abstract
As a high-quality forestry product with excellent performance, particleboard is widely used in many fields and has great market potential. At present, it is a research difficulty to accurately identify small, fine and different shapes of defects on the whole particleboard through machine vision technology. The application of image super-resolution reconstruction technology on particleboard can improve the surface image quality of particleboard, which is conducive to the subsequent improvement of defect detection accuracy. In this study, super-resolution dense attention generative adversarial network (SRDAGAN) model was improved to solve the problem that super-resolution generative adversarial network (SRGAN) reconstructed image would produce artifacts and its performance needed to be improved. The Batch Normalization (BN) layer was removed, the convolutional block attention module (CBAM) was optimized to construct dense block, and the dense blocks were constructed by densely skip connection. Then, the corresponding 52400 image blocks with high resolution and low resolution were trained, verified and tested according to the ratio of 3:1:1. The model was comprehensively evaluated from the effect of image reconstruction and the two indexes of PSNR and SSIM. It was found that compared with BICUBIC and SRGAN, PSNR index of SRDAGAN increased by 4.88dB and 3.25dB respectively, and SSIM increased by 0.0507 and 0.1122 respectively. The reconstructed images not only had clearer texture, but also had more realistic expression of various features, and the performance of the model had been greatly improved. At the same time, this study also emphatically discussed on the image reconstruction effect with defects. The result showed that the SRDAGAN proposed in this study can complete the super-resolution reconstruction of particleboard images with high quality. In the future, it can also be further combined with defect detection for actual production to improve the quality of forestry products and increase economic benefits.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
