2026-04-17T14:58:47Zhttps://recercat.cat/oai/request

oai:recercat.cat:20.500.12327/8322025-10-22T11:23:35Zcom_2072_4428com_2072_4427col_2072_487898

00925njm 22002777a 4500 dc ElMasry, Gamal author Gou, Pere author Al-Rejaie, Salim author 2020-05-24 Specularity or highlight problem exists widely in hyperspectral images, provokes reflectance deviation from its true value, and can hide major defects in food objects or detecting spurious false defects causing failure of inspection and detection processes. In this study, a new non-iterative method based on the dichromatic reflection model and principle component analysis (PCA) was proposed to detect and remove specular highlight components from hyperspectral images acquired by various imaging modes and under different configurations for numerous agro-food products. To demonstrate the effectiveness of this approach, the details of the proposed method were described and the experimental results on various spectral images were presented. The results revealed that the method worked well on all hyperspectral and multispectral images examined in this study, effectively reduced the specularity and significantly improves the quality of the extracted spectral data. Besides the spectral images from available databases, the robustness of this approach was further validated with real captured hyperspectral images of different food materials. By using qualitative and quantitative evaluation based on running time and peak signal to noise ratio (PSNR), the experimental results showed that the proposed method outperforms other specularity removal methods over the datasets of hyperspectral and multispectral images. ElMasry, Gamal, Pere Gou, and Salim Al-Rejaie. 2020. "Effectiveness Of Specularity Removal From Hyperspectral Images On The Quality Of Spectral Signatures Of Food Products". Journal Of Food Engineering, 110148. Elsevier BV. doi:10.1016/j.jfoodeng.2020.110148. 0260-8774 http://hdl.handle.net/20.500.12327/832 https://doi.org/10.1016/j.jfoodeng.2020.110148 Effectiveness of specularity removal from hyperspectral images on the quality of spectral signatures of food products