Three-dimensional super-resolution correlation-differential confocal microscopy with nanometer axial focusing accuracy

We present a correlation-differential confocal microscopy (CDCM), a novel method that can simultaneously improve the three-dimensional spatial resolution and axial focusing accuracy of confocal microscopy (CM). CDCM divides the CM imaging light path into two paths, where the detectors are before and after the focus with an equal axial offset in opposite directions. Then, the light intensity signals received from the two paths are processed by the correlation product and differential subtraction to improve the CM spatial resolution and axial focusing accuracy, respectively. Theoretical analyses and preliminary experiments indicate that, for the excitation wavelength of λ = 405 nm, numerical aperture of NA = 0.95, and the normalized axial offset of uM = 5.21, the CDCM resolution is improved by more than 20% and more than 30% in the lateral and axial directions, respectively, compared with that of the CM. Also, the axial focusing resolution important for the imaging of sample surface profiles is improved to 1 nm

Improving spatial resolution of confocal Raman microscopy by super-resolution image restoration

A new super-resolution image restoration confocal Raman microscopy method (SRIR-RAMAN) is proposed for improving the spatial resolution of confocal Raman microscopy. This method can recover the lost high spatial frequency of the confocal Raman microscopy by using Poisson-MAP super-resolution imaging restoration, thereby improving the spatial resolution of confocal Raman microscopy and realizing its super-resolution imaging. Simulation analyses and experimental results indicate that the spatial resolution of SRIR-RAMAN can be improved by 65% to achieve 200 nm with the same confocal Raman microscopy system. This method can provide a new tool for high spatial resolution micro-probe structure detection in physical chemistry, materials science, biomedical science and other areas

Confocal Raman image method with maximum likelihood method

With the increasing interest in nano microscopic area, such as DNA sequencing, micro structure detection of molecular nano devices, a higher requirement for the spatial resolution of Raman spectroscopy is demanded. However, because of the weak Raman signal, the pinhole size of confocal Raman microscopy is usually a few hundreds microns to ensure a relatively higher spectrum throughput, but the large pinhole size limits the improvements of spatial resolution of confoal Raman spectroscopy. As a result, the convential confocal Raman spectroscopy has been unable to meet the needs of science development. Therefore, a confocal Raman image method with Maximum Likelihood image restoration algorithm based on the convential confocal Raman microscope is propose. This method combines super-resolution image restoration technology and confocal Raman microscopy to realize super-resolution imaging, by using Maximum Likelihood image restoration algorithm based on Poisson-Markov model to conduct image restoration processing on the Raman image, and the high frequency information of the image is recovered, and then the spatial resolution of Raman image is improved and the super-resolution image is realized. Simulation analyses and experimental results indicate that the proposed confocal Raman image method with Maximum Likelihood image restoration algorithm can improve the spatial resolution to 200 nm without losing any Raman spectral signal under the same condition with convential confocal Raman microscopy, moreover it has strong noise suppression capability. In conclusion, the method can provide a new approach for material science, life sciences, biomedicine and other frontiers areas. This method is an effective confocal Raman image method with high spatial resolution

Exploring Generative Neural Temporal Point Process

Temporal point process (TPP) is commonly used to model the asynchronous event sequence featuring occurrence timestamps and revealed by probabilistic models conditioned on historical impacts. While lots of previous works have focused on `goodness-of-fit' of TPP models by maximizing the likelihood, their predictive performance is unsatisfactory, which means the timestamps generated by models are far apart from true observations. Recently, deep generative models such as denoising diffusion and score matching models have achieved great progress in image generating tasks by demonstrating their capability of generating samples of high quality. However, there are no complete and unified works exploring and studying the potential of generative models in the context of event occurence modeling for TPP. In this work, we try to fill the gap by designing a unified \textbf{g}enerative framework for \textbf{n}eural \textbf{t}emporal \textbf{p}oint \textbf{p}rocess (\textsc{GNTPP}) model to explore their feasibility and effectiveness, and further improve models' predictive performance. Besides, in terms of measuring the historical impacts, we revise the attentive models which summarize influence from historical events with an adaptive reweighting term considering events' type relation and time intervals. Extensive experiments have been conducted to illustrate the improved predictive capability of \textsc{GNTPP} with a line of generative probabilistic decoders, and performance gain from the revised attention. To the best of our knowledge, this is the first work that adapts generative models in a complete unified framework and studies their effectiveness in the context of TPP. Our codebase including all the methods given in Section.5.1.1 is open in \url{https://github.com/BIRD-TAO/GNTPP}. We hope the code framework can facilitate future research in Neural TPPs

Synchronous nanoscale topographic and chemical mapping by differential-confocal controlled Raman microscopy

Confocal Raman microscopy is currently used for label-free optical sensing and imaging within the biological, engineering, and physical sciences as well as in industry. However, currently these methods have limitations, including their low spatial resolution and poor focus stability, that restrict the breadth of new applications. This paper now introduces differential-confocal controlled Raman microscopy as a technique that fuses differential confocal microscopy and Raman spectroscopy, enabling the point-to-point collection of three-dimensional nanoscale topographic information with the simultaneous reconstruction of corresponding chemical information. The microscope collects the scattered Raman light together with the Rayleigh light, both as Rayleigh scattered and reflected light (these are normally filtered out in conventional confocal Raman systems). Inherent in the design of the instrument is a significant improvement in the axial focusing resolution of topographical features in the image (to ∼1 nm ), which, when coupled with super-resolution image restoration, gives a lateral resolution of 220 nm. By using differential confocal imaging for controlling the Raman imaging, the system presents a significant enhancement of the focusing and measurement accuracy, precision, and stability (with an antidrift capability), mitigating against both thermal and vibrational artefacts. We also demonstrate an improved scan speed, arising as a consequence of the nonaxial scanning mode