We propose a novel pipeline and related software tools for processing the multi-light image collections (MLICs) acquired in different application contexts to obtain shape and appearance information of captured surfaces, as well as to derive compact relightable representations of them. Our pipeline extends the popular Highlight Reflectance Transformation Imaging (H-RTI) framework, which is widely used in the Cultural Heritage domain. We support, in particular, perspective camera modeling, per-pixel interpolated light direction estimation, as well as light normalization correcting vignetting and uneven non-directional illumination. Furthermore, we propose two novel easy-to-use software tools to simplify all processing steps. The tools, in addition to support easy processing and encoding of pixel data, implement a variety of visualizations, as well as multiple reflectance-model-fitting options. Experimental tests on synthetic and real-world MLICs demonstrate the usefulness of the novel algorithmic framework and the potential benefits of the proposed tools for end-user applications.

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The analysis of surface roughness in highly reflective metal artworks is challenging and requires contactless devices capable to measure regions with high micrometer accuracy in both depth and lateral directions. We demonstrate optical profilometry based on scanning conoscopic holography for micrometer measurement of silver samples treated with different hand-made cleaning processes. The technique is shown effective in acquiring shiny and smooth metal samples providing high-resolution and high-accurate dataset ($$0.1\,\upmu \hbox {m}$$ 0.1 μ m depth and $$5\,\upmu \hbox {m}$$ 5 μ m lateral resolution) that is a reliable representation of the microsurface structure. From a statistical point of view, the cleaning treatments have the same nature of the low-abrasion, but the underlying mechanical processes are different. This fact suggested a more in-depth study of both the amplitude and the hybrid areal roughness parameters. It is proposed a workflow for a dual integrated multiscale roughness analysis for surface characterization: a scale inspection to detect possible texture non-homogeneity, and a signals separation to outline the most significant texture components. The scale-limited components allowed to discriminate the different surface processes. The results on silver samples demonstrate the potential of multiscale roughness analysis by conoscopic holography as a new tool for treatment monitoring in metal artworks.

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