Abstract
In this paper, we propose a methodology for the fusion of shape from focus and reflectance transformation imaging. This fusion of two seemingly disparate methods of computational imaging is proposed with the purpose of leveraging their strengths in understanding overall surface structure (low-frequency detail) and surface texture/micro-geometry (high-frequency detail), respectively. This fusion is achieved by our new proposal of the integration of varying light images at different focus distances. We compare three methods of integration: the mean gradient response, the maximum gradient response, and the full vector gradient (FVG). The validation of the tested methods was conducted using different focus measure window sizes and multi-light integration methods to provide a clear demonstration of the effectiveness of the proposed method. The FVG is determined to provide a higher-quality shape recovery of a complex object with the trade-off of increasing the scope of the image acquisition.
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