Affiliation:
1. School of Mechanical Engineering, Zhejiang Sci-Tech University, China
2. School of Information Science and Engineering, Zhejiang Sci-Tech University, Hangzhou, Zhejiang, China
Abstract
Hyperspectral images possess abundant information and play a pivotal role in enhancing the accuracy of color difference detection in textiles. However, traditional hyperspectral imaging methods necessitate costly equipment and intricate operational procedures. A novel deep learning model based on a multihead attention mechanism was proposed in this article to facilitate the extensive application of hyperspectral imaging technology in textile quality inspection. This model enabled the reconstruction of the hyperspectral information of plain weave textiles from a single RGB image. In this model, encoder-decoder architecture and pyramid pooling convolutional operations were employed to integrate multiscale features of plain weave cotton-linen textiles. This could capture details and contextual information in textile images more precisely, enhancing the accuracy of hyperspectral image reconstruction. Simultaneously, an attention mechanism was introduced to increase the model’s receptive field and improve its focus on key regions in the input image and feature maps. This resulted in a reduced weighting of redundant information during network learning, leading to an improved feature extraction capability of the network. Through these methods, successful reconstructions of plain weave textiles hyperspectral information from a single RGB image was achieved. Quantitative and qualitative tests were conducted on two datasets, namely, the NTIRE 2020 dataset and a self-made textile dataset, to evaluate the performance of the proposed method. The approach proposed in this article exhibited promising results on both datasets. Specifically, the reconstructed textile hyperspectral images achieved a root mean square error of 0.0344, a peak signal-to-noise ratio of 29.945, a spectral angle mapper of 3.753, and a structural similarity index measure of 0.955 on the textile dataset. In the reconstructed hyperspectral colorimetric experiment, the maximum value of average color difference was 2.641. These results demonstrate that the method can meet the requirements for textile color measurement applications.
Funder
the Key R&D projects of Science and Technology Department of Zhejiang Province
National Natural Science Foundation of China
Reference24 articles.
1. Multi-color measurement of printed fabric using the hyperspectral imaging system
2. Near-infrared hyperspectral imaging of lamination and finishing processes in textile technology
3. Parmar M, Lansel S, Wandell BA. Spatio-spectral reconstruction of the multispectral datacube using sparse recovery. In: 21st ACM Conference on Computer and Communications Security, Scottsdale, AZ, United States, 3–7 November 2014, pp. 19–34.
4. Arad B, Ben-Shahar O. Sparse recovery of hyperspectral signal from natural RGB images. In: Computer vision-ECCV 2016: 14th European conference, Amsterdam, The Netherlands, 11–14 October 2016, Proceedings, Part VII 14. Springer International Publishing, pp. 19–34.
5. Aeschbacher J, Wu J, Timofte R. In defense of shallow learned spectral reconstruction from RGB images. In: Proceedings of the IEEE international conference on computer vision workshops (ICCVW), Venice, Italy, 22–29 October 2017, pp. 471–479.