Soft‐Rigid Unified Structure Textile In article number 2213419, Swee Ching Tan, Songlin Zhang, Yan Ma, and co‐workers developed a composite textile with a soft‐rigid unified structure (SRUS) design inspired by crocodile skin. The SRUS textile combines excellent protective properties and wearing comfort, providing a new approach to personal protective equipment development. The study shows the potential of biomimetic design in creating innovative functional textiles.

In the work, the Contourlet transform is modified for a more complex situation in order to obtain a subtler decomposition of the warp-knitted fabric image. The new Contourlet transform is named the non-subsampled wavelet-packet-based Contourlet transform (NWPCT) and it consists of wavelet-packet transform and a non-subsampled directional filter bank. Firstly, the fabric image is processed by means of wavelet-packet transform with segmented threshold de-noising to acquire the subtle frequency coefficients. Secondly, the more elaborate directional coefficients will be obtained by decomposing the wavelet-packet coefficients with non-subsampled directional filter bank. Then the directional coefficients with higher energy are chosen to reconstruct the wavelet-packet coefficients. Finally, the iterative threshold method and object operation based on morphology are applied to segment the defect profile. The final experimental result demonstrates that NWPCT has excellent properties to segment out the defects (broken wrap, oil and width barrier). The defect profile is distinct enough for the further work concerning warp-knitted fabric defect recognition.

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The Shearlet transform has been a burgeoning method applied in the area of image processing recently which, differing from the Wavelet transform, has excellent properties in processing singularities for multidimensional signals. Not only is it similar to the performance of the Curvelet transform, it also overcomes the disadvantage of the Curvelet transform with respect to discretization. In this paper, the Shearlet transform with segmented threshold de-nosing is proposed to segment a warp-knitted fabric defect. Firstly a warp-knitted fabric image of size 512*512 is filtered by the Laplacian Pyramid transform and decomposed into low frequency and high frequency coefficients. Secondly the high frequency coefficients are operated with a pseudo-polar grid and then convoluted by the window function. Thirdly the shearlet coefficients will be obtained through redefining the Cartesian coordinates from the pseudo-polar grid coordinates and de-noised by the segmented threshold method. Then the coefficients which have high energy are selected for reconstruction in an inverse way using the previous steps. Finally the iterative threshold method and object operation based on morphology are applied to segment out the defect profile. The experiment’s result states that the Shearlet transform shows excellent performance in segmenting a common warp-knitted fabric defect, indicating that the segment results can be applied for further defect automatic recognition.

In this paper, a non-subsampled wavelet-based contourlet transform (NWCT) is applied in warp-knitted fabric defect segmentation. Compared with the traditional contourlet transform, wavelet transform takes the place of Laplacian pyramid in NWCT and the directional filter bank is non-subsampled. The wavelet transform with improved wavelet threshold is put to use, and the original fabric image can be decomposed into low-frequency approximate coefficient A and high-frequency detail coefficients V, H, and D. The high-frequency detail coefficients are processed by the non-subsampled directional filter bank to get directional sub-band coefficients. Afterward, the effective sub-band coefficients based on regional energy are chosen to reconstruct V, H, and D. And the reconstructed fabric image will be achieved by inverse non-subsampled wavelet-based contourlet transform. The adaptive threshold method and morphological processing are used to obtain the legible defect profile. The experiment demonstrates that NWCT can achieve the positive segmentation regarding the common defects, such as broken warp, width barrier, and oil, and has excellent performance on these directional defects and regional defects. It is acknowledged that NWCT will provide a new way to detect warp-knitted fabric defects automatically.