Abstract
Since the invention of microscope, it has been one of the most important equipments in medical and biological technology communities. For medical applications, doctors investigate patients’tissues through microscopes, which help them better diagnose disease and decrease possibility of errors. For biologists, microscopes help them recognize things through micro world, understand how living creature works in detail. In summary, microscopy has great advantage to nowaday technology development.There are many kinds of microscopes, each with different resolutions, but eventually they are all in essence developed to observe images more clearly with more details. Price, of course, is one of the concerns. This research is intended to increase the resolution of 3D microscopy images by image processing algorithms.The factors affecting the captured resolution can be complex, including the backlight of the scene, noises of the sensor, pixel rate of the camera, and even the sample itself affects the final resolution due to defocus. This thesis is mainly focused on confocal fluorescence microscopy, capturing defocused 3D images (a series of 2D image stack), and then further image processing on the 3D image with 3D deconvolutionto to produce the 3D super-resolution sample.In the whole algorithm, this study focuses on processing the images of biological tissue from light microscopes. Since the imperfection of lenses results in blurriness and distortion of the original sample, Blind Deconvolution will be employed to estimate Point Spread Function on the raw 3D data for subsequent 3D reconstruction to alleviate these problems.