As industrial and scientific advancements continue, the demand for precise measurement of three-dimensional (3D) shapes and surfaces is steadily increasing. However, accurate 3D measurement of certain surfaces, especially those with varying reflectivities, has always been a challenging issue. Multi-exposure fusion methods have shown stable, high-quality measurement results, but the selection of parameters for these methods has largely been based on experience. To address this issue, this paper has improved the multi-exposure fusion method and introduced a guided approach for parameter selection, significantly enhancing the completeness of measurement results. Additionally, a comparative model is developed to experimentally validate the specific impacts of Gaussian window variance, optimal grayscale range, and attenuation factor variance on the integrity of 3D reconstruction. The experimental results demonstrate that under the guidance of the parameter adjustment method proposed in this paper, the multi-exposure fusion for measuring the 3D topography of high-dynamic surfaces improves the restoration coverage from the original 86% (bright areas) and 50% (dark areas) to over 99%. This provides a selection strategy for parameter adjustment guidance in precise measurements based on the multi-exposure method.
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http://dx.doi.org/10.1364/AO.517940 | DOI Listing |
The limited dynamic range of the detector can impede coherent diffractive imaging (CDI) schemes from achieving diffraction-limited resolution. To overcome this limitation, a straightforward approach is to utilize high dynamic range (HDR) imaging through multi-exposure image fusion (MEF). This method involves capturing measurements at different exposure times, spanning from under to overexposure and fusing them into a single HDR image.
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Due to sensor limitations, the light field (LF) images captured by the LF camera suffer from low dynamic range and are prone to poor exposure. To solve this problem, combining multi-exposure technology with LF camera imaging can achieve high dynamic range (HDR) LF imaging. However, for dynamic scenes, this approach tends to produce disturbing ghosting artifacts and destroy the parallax structure of the generated results.
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Accurately capturing dynamic scenes with wide-ranging motion and light intensity is crucial for many vision applications. However, acquiring high-speed high dynamic range (HDR) video is challenging because the camera's frame rate restricts its dynamic range. Existing methods sacrifice speed to acquire multi-exposure frames.
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In this paper, we present a novel high dynamic range (HDR)-like image generator that utilizes mutual-guided learning between multi-exposure registration and fusion, leading to promising dynamic multi-exposure image fusion. The method consists of three main components: the registration network, the fusion network, and the dual attention network which seamlessly integrates registration and fusion processes. Initially, within the registration network, the estimation of deformation fields among multi-exposure image sequences is conducted following an exposure-invariant feature extraction phase.
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