Thermal infrared cameras are devices which are equipped with a sensor which can receive the thermal infrared radiation of the most external layer of the surface of the object and then create a thermal infrared (TIR) image, also called a thermogram. Infrared thermography (IRT) is a non-contact, non-destructive, and non-invasive technique which can be carried out using measuring devices that are able to receive radiation (of a certain wavelength) from an object which has a temperature higher then absolute zero. The details of the designed methods, as well as the experiments related to the methods, are presented in this article. The second method was termed reprojection and the result of this method is a point cloud augmented by thermal infrared information. One of the methods was termed sharpening and the result of this method is mainly an augmented orthophoto, and an augmented texture of the 3D model. Both methods require a fixed system of TIR and RGB cameras and for each TIR image a corresponding RGB image must be captured. To eliminate these negative factors, two methods of photogrammetric co-processing of TIR and RGB images were designed. Among the negative factors are the lower resolution of TIR images compared to RGB images and lack of visible features on the TIR images. However, photogrammetric processing of TIR images is difficult due to negative factors which are caused by the natural character of TIR images. On the augmented 2D and 3D results, it is possible to locate thermal occurrences in the coordinate system and to determine their scale, length, area or volume. By the photogrammetric processing of thermal infrared (TIR) images, it is possible to create 2D and 3D results augmented by thermal infrared information. This demonstrates that with our optimized workflow we reach a resolution that is comparable to 3D-SIM-imaging, but with better depth penetration for confocal images of beads and the biological sample.In some applications of thermography, spatial orientation of the thermal infrared information can be desirable. We measure a resolution increase of approximately 2-fold in the lateral and 3-fold in the axial direction throughout a depth of 60 μm. Subsequently, we apply the parameters to the imaging and data restoration of fluorescently labelled meiotic spindles of mouse oocytes. We validate our workflow by imaging sub resolution fluorescent beads and measuring the maximum lateral and axial resolution of the confocal system. We compare two different deconvolution algorithms, evaluate the necessity of denoising and establish the optimal image restoration procedure. Our workflow comprises refractive index matching, the optimization of microscope hardware parameters and image restoration by deconvolution.

We therefore developed an image processing workflow that allows improving the lateral and axial resolution of a standard confocal microscope. In the presented work we aimed at improving confocal imaging to obtain highest possible resolution in thick biological samples, such as the mouse oocyte.

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