Textile composition identification (TCI) is an essential basic link in the textile industry. Methods based on computer vision or near-infrared (NIR) signal processing have shown potential for the nondestructive TCI task. However, these methods ignore that the integration of NIR signals and visual information may help the model learn a better representation through information complementarity. This paper propose a Multi-Signal Perception Network (MSPNet) for nondestructive textile composition identification, allowing the model to benefit from the advantages of multimodal data. Firstly, a two-way feature extraction network is used to obtain multi-modal features. After that, we propose a multimodal signal fusion module to control the aggregation granularity among multimodal data. Specifically, the target areas of the image are perceived by a target area perception module (TAP). Then a bi-gated aggregation (Bi-GFA) is designed to capture consistent semantic information from signal to image and image to signal. The quantitative and qualitative results of the proposed MSPNet are significantly improved compared to both single and multimodal approaches.

Deep learning techniques have recently made considerable progress in image inpainting by introducing prior knowledge, e.g. texture and structure. However, the existing methods still suffer from artefacts such as distorted texture and abrupt colors due to insufficient consideration of correlation between the prior visual features. In this paper, we propose a novel progressive and multi-prior-guided network (PMPN) for image inpainting inspired by the human painting process, which first constructs the sketch of painting art, then generates the corresponding textures finally fills the colors to the appropriate locations. In particular, to model the global multi-scale contexts during the reconstruction process, we design a bi-directional cross-stage perception module that captures spatial information across branches and stages and guides the model to synthesize a natural and consistent texture. Our proposed PMPN network is evaluated on three publicly available datasets, outperforming the current state-of-the-art models.

Identifying fiber compositions is an important aspect of the textile industry. In recent decades, near-infrared spectroscopy has shown its potential in the automatic detection of fiber components. However, for plant fibers such as cotton and linen, the chemical compositions are the same and thus the absorption spectra are very similar, leading to the problem of "different materials with the same spectrum, whereas the same material with different spectrums" and it is difficult using a single mode of NIR signals to capture the effective features to distinguish these fibers. To solve this problem, textile experts under a microscope measure the cross-sectional or longitudinal characteristics of fibers to determine fiber contents with a destructive way. In this paper, we construct the first NIR signal-microscope image textile fiber composition dataset (NIRITFC). Based on the NIRITFC dataset, we propose an image-signal correlation network (ISiC-Net) and design image-signal correlation perception and image-signal correlation attention modules, respectively, to effectively integrate the visual features (esp. local texture details of fibers) with the finer absorption spectrum information of the NIR signal to capture the deep abstract features of bimodal data for nondestructive textile fiber identification. To better learn the spectral characteristics of the fiber components, the endmember vectors of the corresponding fibers are generated by embedding encoding, and the reconstruction loss is designed to guide the model to reconstruct the NIR signals of the corresponding fiber components by a nonlinear mapping. The quantitative and qualitative results are significantly improved compared to both single and bimodal approaches, indicating the great potential of combining microscopic images and NIR signals for textile fiber composition identification.