Affiliations: [a]
College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, China
| [b]
College of Petroleum Engineering, Harbin Institute of Petroleum, Harbin, China
Correspondence:
[*]
Corresponding author. Liping Sun, College of Mechanical and Electrical Engineering, Northeast Forestry University, Harbin, 150040, Heilongjiang, China. E-mail: [email protected].
Abstract: Veneer is the critical raw material for manufacturing man-made board products, therefore the quality of the veneer determines the level of the man-made board. However, defects in the veneer may significantly lower its grade. Currently, identifying veneer defects requires manual inspection and subsequent inpainting using a veneer digging machine. Unfortunately, this method only removes the defects of the veneer but ignore the consistency of its texture. To address this issue, we propose a feasible veneer defect reconstruction method that utilizes a texture-aware-multiscale-GAN architecture. Our method performs texture reconstruction of veneer defects to increase the texture information of the reconstructed image while improving the model efficiency, so that generates natural-looking textures in the reconstructed defect areas. The model is trained by end-to-end updating of four cascades of efficient generators and discriminators. We also employed a loss function based on local binary patterns (LBP) to ensure that the restored images contain sufficient texture information. Finally, region normalization is used in the model to enhance the accuracy of the model. Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM) are used to evaluate the effectiveness of image restoration, the results show that PSNR of the method reacheds 35.32 and SSIM reaches 0.971. By minimizing the difference between the generated texture and that of the original image, our model produces high-quality results.
