Bidirectional Reflectance Distribution Function (BRDF)-Based Coarseness Prediction of Textured Metal Surface

Wanhee Han, Jinsang Lim, Seung Jae Lee, Cheol Young Kim, Wan Chin Kim, No Cheol Park

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


Because coarseness has a remarkable effect on visual appearance, there has been an increasing demand for measuring quantitative coarseness based on an explicit algorithm, especially in industry. However, even in BYK-mac, a reliable measurement device for high correlation with visual inspection, its results are not clearly defined and not traceable to international standards. In addition, previous studies requiring a captured image of a sample that is sensitive to capturing conditions are not appropriate to apply to industrial sites. Therefore, in this paper, a novel method for predicting coarseness by measuring only the color and the bidirectional reflectance distribution function (BRDF) is presented. The proposed method generates images using illumination optical analysis by applying the BRDF, which is insensitive to the capturing conditions, so it can be reliably applied to industrial sites with various measurement disturbances. The proposed coarseness prediction workflow was verified based on a comparison of the predicted coarseness values of 56 samples with seven levels of roughness for each of the eight colors and the measured values using BYK-mac. Images generated from illumination optical analysis through BRDF of the prepared sample showed lower brightness on the rougher surface, similar to the BRDF measurement results. The results of the measurement and comparison show that the coarseness orders for each color are similar to each other and that the coarseness variations according to roughness levels are also similar, with a coefficient of determination, R2, of 0.9374.

Original languageEnglish
Pages (from-to)32461-32469
Number of pages9
JournalIEEE Access
Publication statusPublished - 2022

Bibliographical note

Publisher Copyright:
© 2013 IEEE.

All Science Journal Classification (ASJC) codes

  • General Engineering
  • General Computer Science
  • General Materials Science


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