Super-resolution microscopy CARS

Abstract

Super-resolution microscopy techniques, which overcome the diffraction limit of optical microscopy, are revolutionizing biological and non-biological research areas by providing researchers the ability to observe structures (materials, cells and tissues) at nanometer resolutions. The attainment of super-resolution through a kernel modified subtraction scheme is demonstrated. The conventional subtraction of Gaussian and Doughnut beams produces a negative peak that hinders the acquisition of super-resolution. Super-resolution is significant because it permits the CARS procedure to utilize both Gaussian and doughnut beams. We report a kernel modified subtraction scheme to enhance super-resolution that can be used with anti-Stokes Raman scattering (CARS) microscopy. Hypothetical and trial examinations show that this interesting technique is ideal for the presentation of high resolution. We wrote computer programs in order to analyze and improve the resolution of the images obtained. One of the programs we created is a kernel algorithm to reduce the negative peaks that occur in conventional subtraction. An amount examination is performed in order to set an ideal estimation of the kernel size to build adequate resolution. Additionally, the beam width of the Gaussian PSF is varied, and the resulting variation in resolution is considered. We analyzed the images of a large number of PSFs and performed a similar examination of the PSFs and images in the presence of noise. Additionally, the investigation of complex samples was undertaken. We have also applied particle swarm optimization to the modified subtraction kernel scheme. Finally, this strategy can be utilized with CARS microscopy because it is also a non–linear procedure and its usage with the proposed kernel modified subtraction scheme has the possibility of increasing the resolution.