In this article, we discuss some current trends in quantum microscopy. The new imagining technologies on cell and molecular resolution levels become promising tools in the analysis and study of biological processes in physiology and neuroscience.

Handling a large volume of microscopy data requires, in our opinion, a multi-scale approach to adaptive signal filtering, communication, and storage. We discuss here, subsequently, some technical aspects of applying quantum fields adaptive signal processing.

Quantum fluorescence microscopy

Two-photon absorption effect was initially theorized by Maria Goeppert-Mayer, which has been applied in quantum microscopy. It has opened a window to imagining, analyzing, and study of biological processes with a significant increase in data resolution. Quantum fluorescence microscopy makes it possible to acquire figure-background contents of different signal frequencies at the cell and molecular resolution levels, utilizing a noninvasive optical biopsy.

A quantum fields adaptive signal filtering in multiple of scales

In the theory of stochastic resonance synergistic we have derived a Green function that optimizes filtering criteria in scale-space1-3. At a given scale, the filter bandwidth is given by the generalized uncertainty relation.

Two-dimensional, up and down scale-space waves are brought into a stochastic resonance dynamically, at the scale dimension β. At the stochastic resonances, multidimensional information is expanded along 5-dimensional manifolds, in a hierarchy of scale spaces. The resonate waves couple information synergistically, satisfying the mass conservation principle. The information transfer is preserved by the scale-space wave information tunneling.

A stochastic interpretation of stereograms along a scale dimension has been described4. A pair of textured images with two distinct regions of textural elements are fused in a stochastic equilibrium. These 5d stereograms have been shown for different levels of figure-background spatial frequencies and textural element differences. A recurrent scheme of adaptive filtering has been studied in visual textures segmentation5.

Holographic representation of the quantum fields' information carriers, in 3-dimensional space, suggests the emergence of multiple fields, not limited to conventionally four. The equations describing the coupling of gravity, electromagnetic, strong, and weak fields in multiple scales have been shown to give only partial answers in 4-dimensional space-time.

Progressive transmission, reconstruction, and storage of hierarchically segmented data streams

The scale-space approach makes the hierarchy of bipartite segments suitable for progressive signal transmission and reconstruction in finer details. It enables optimal trade-off usage of available transmission bandwidth and computing power. Storage and retrieval of information with this approach makes data available for enhanced visualization in the hierarchy of computed scales-space coordinate frames.

We have described a hierarchical scale quantization algorithm for multispectral still images6, and motion information7.

Concluding remarks

Current trends in quantum microscopy that utilize the two-photon absorption effect significantly increase data resolution and open a window to imagining, analysis, and study of biological processes on cell and molecular levels. In this article, we have reflected on some technical aspects of handling a large volume of data by quantum fields adaptive signal processing and communication on multiple scales. Communication streaming, reconstruction, enhanced visualization, and storage of hierarchically segmented data streams have been discussed.

References

1 Jovovic, M., Stochastic Resonance Synergetics – Quantum Information Theory for Multidimensional Scaling, Journal of Quantum Information Science, 5/2:47-57, 2015.
2 Jovovic, M., and G. Fox, Multi-dimensional data scaling – dynamical cascade approach, Indiana University, 2007.
3 Jovovic, M., H. Yahia, and I. Herlin, Hierarchical scale decomposition of images – singular features analysis, INRIA, 2003.
4 Jovovic, M., A Markov random fields model for describing unhomogeneous textures: generalized random stereograms. IEEE Workshop Proceedings on Visualization and Machine Vision, and IEEE Workshop Proceedings on Biomedical Image Analysis, Seattle, 1994.
5 Jovovic, M., Texture Discrimination by Adaptive Filtering, 17th. European Conference on Visual Perception, Eindhoven, 1994.
6 Jovovic, M., Space-Color Quantization of Multispectral Images in Hierarchy of Scales, Int. Conf. on Image Processing, Thessaloniki, Greece, pp. 914-917, 2001.
7 Jovovic, M., Image segmentation for feature selection from motion and photometric information by clustering, SPIE Symposium on Visual Information Processing V, Orlando, 1996.