Computational self-corrected quantitative 3D topographic imaging.

Three-dimensional microscopy has become an essential tool for inspecting samples across a broad range of fields, from scientific research to industrial applications. In many cases, precise geometric measurements on 3D shapes and structures at the micro scale are vital. For this, established techniques typically rely on axial scanning to sample the measured volume. However, an imperfect motion of the scanner inevitably introduces errors in the output measurements. We present a method to estimate and suppress the scanner positioning errors through computational analysis of the acquired data, leading to improved measurement precision. While methods for correction of such errors are available and well known for interferometric systems thanks to fringe analysis, this has remained an unsolved challenge for non-interferometric technologies such as confocal microscopy. We experimentally demonstrate the method and report a ten-fold improvement in the axial precision of confocal microscopy systems equipped with motorised scanners. The results are comparable to or even surpass those achieved with high-quality piezoelectric scanners, while preserving the large measurement ranges offered by motorised linear stages. Furthermore, this method offers a cost-effective alternative to high-quality scanners by leveraging low-cost computation in place of expensive hardware, and it can be seamlessly integrated into existing systems with minimal modification.