Effectiveness of specularity removal from hyperspectral images in the colour spectral measurement of wool fibres

Microscopic hyperspectral imaging technology is a potential non-destructive and non-contact method for colour measurement of micrometre-sized textile fibres. However, specularity on the fibre surface can distort the accurate colour information and affect the accuracy of the colour measurement. This paper proposed a specular-constrained sparse approximation (SCSA) for specular-diffuse reflection separation from hyperspectral images of wool fibres. First, a specular prior map is generated based on the lightness dissimilarity. Then the SCSA model is used to decompose the processed hyperspectral image A into low-rank data L, sparse specularity data S constrained by the specular prior map, sparse noise E, and Gaussian noise N. A non-linear logistic sigmoid function and a sparse approximation of A - L - N to S are used to improve the performance of specularity removal during iterative optimization. The experimental results show that the proposed method significantly preserves diffuse reflectance and texture details in the specular highlight regions to obtain actual spectral reflectance and chromatic values from hyperspectral images of wool fibres.