Virtual SPOM Microscopy Using Software Filters for Resolution
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Solution Overview
Problem
Conventional spatial overlap modulation microscopy (SPOM) suffers from low temporal resolution, high laser 1/f noise, and limitations in spatial frequency, making it unsuitable for real-time in vivo imaging and single-beam microscopy systems, with challenges in achieving isotropic modulation and improving signal strength without degrading resolution.
Innovation Solution
The implementation of virtual SPOM (vSPOM) using software to simulate the effect of optical microscopy, applying filters like Gabor, cosine, Laplacian of Gaussian (LoG), and radial filters to oversampled images, enabling isotropic modulation and enhancing resolution without the need for hardware-based beam splitting and recombination, thereby improving image quality and reducing noise.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional spatial overlap modulation microscopy (SPOM) is used to achieve super-resolution imaging, then resolution is improved, but temporal resolution deteriorates and laser 1/f noise increases
Solution Approach 1:
The patent applies virtual SPOM (vSPOM) which uses software-based filtering to simulate the effect of optical modulation microscopy, creating a virtual copy of the super-resolution effect without requiring the complex hardware modulation and demodulation processes. This copying approach achieves similar resolution improvement while avoiding the temporal resolution loss and noise issues of conventional SPOM
Solution Approach 2:
The patent replaces the mechanical/optical modulation system with a computational filtering system. Instead of using physical beam modulation and detection hardware, the invention uses software filters (Gabor, cosine, LoG, radial) applied to oversampled images to achieve the same super-resolution effect, thereby eliminating the temporal resolution bottleneck and laser noise issues
2Measurement precision
If hardware-based beam splitting and recombination is used in SPOM, then spatial frequency modulation is achieved, but device complexity increases
Solution Approach 1:
The patent creates a virtual implementation of beam modulation effects through software filtering. Instead of physically splitting and recombining beams with complex optical hardware, the invention applies computational filters to oversampled images to simulate the spatial frequency modulation effect, dramatically reducing device complexity while maintaining resolution benefits
Solution Approach 2:
The patent substitutes the mechanical beam splitting and recombination hardware with a computational image processing system. The complex optical modulation pathway is replaced by applying mathematical filters (Gabor, cosine, LoG, radial) to image data, achieving the same spatial frequency analysis without the hardware complexity
3Measurement precision
If conventional SPOM is used to achieve isotropic modulation, then resolution is improved in specific directions, but achieving true isotropic modulation becomes difficult
Solution Approach 1:
The patent implements a universal filtering framework that applies the same computational principles in all spatial directions. The vSPOM approach uses isotropic filters (such as radial and Laplacian of Gaussian filters) that treat all directions equally, achieving true isotropic resolution improvement without the directional limitations of conventional optical modulation methods
Solution Approach 2:
The patent uses virtual filtering to copy the isotropic modulation effect that is difficult to achieve with physical optics. By applying mathematical filters that are inherently isotropic (such as radial symmetry filters) to oversampled images, the invention achieves uniform resolution improvement in all directions without the complexity of implementing isotropic optical modulation
Data Source
AI summary
A method and non-transitory computer readable medium for processing an oversampled image is disclosed. Filters are applied to an oversampled image to obtain a filtered image. The image filters are Gabor filter, cosine filter, laplacian of Gaussian filter, and radial filter. The filtered image can be turned into a displayed image that is displayed. The displayed image can be a 3D image. The displayed image can be refreshed at a rate of about one frame per second.