We show non-invasive 3D plant disease imaging using automated monocular vision-based structure from motion. We optimize the number of key points in an image pair by using a small angular step size and detection in the extra green channel. Furthermore, we upsample the images to increase the number of key points. With the same setup, we obtain functional fluorescence information that we map onto the 3D structural plant image, in this way obtaining a combined functional and 3D structural plant image using a single setup.@en

High-accurate unwrapped phases are demanded in various research fields such as optical imaging optical holography, optical diffraction tomography, and magnetic resonance imaging. However, the ground-truth phase is not accessible due to 2 ambiguity which arises from phase jumps in the wrapped phase. In this study, we propose to improve the accuracy of unwrapping process by increasing the sampling frequency to reconstruct the unwrapped phase with high accuracy for the application of optical imaging. The simulation results show increasing the optical magnification from 4X to 8X enables improvement of the phase estimation accuracy by 51% for a highly refractive object. Experimental results validate the sensitivity of phase estimation on the sampling size. Our approach demonstrates significant achievement in obtaining ground-truth phases for highly refractive objects.

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