<p dir="ltr">Fluorescence microscopy, a cornerstone in biological imaging, faces inherent challenges due to photon budget constraints that affect the signal-to-noise ratio (SNR), ultimately limiting imaging performance. This thesis explores theoretical frameworks to address two fundamental issues: the denoising limit of fluorescence microscopy images and the resolution limit in the presence of photon noise. Firstly, we extend the application of the Cramér-Rao Lower Bound (CRLB) to establish a variance lower bound for image denoising algorithms in fluorescence microscopy. By incorporating constraints specific to the imaging system and biological specimens, we provide a benchmark for evaluating the performance of state-of-the-art denoising algorithms. Our analysis reveals that this lower bound is determined by factors such as photon count, readout noise, detection wavelength, effective pixel size, and numerical aperture of the microscope system. Secondly, building upon the pioneering work by Ernest Abbe and leveraging modern fluorescence and nanoscopy advancements, we propose a novel theoretical framework to quantify the resolving power of fluorescence microscopes under finite photon conditions. This model integrates the traditional diffraction limit with photon statistics to determine the practical resolution limit, highlighting the trade-offs between photon noise and resolution enhancement in techniques like confocal microscopy. This dual approach not only refined the theoretical understanding of fluorescence microscopy's capabilities but also assisted in designing and optimizing more effective imaging protocols. Through these investigations, this thesis provided a comprehensive theoretical foundation for improving fluorescence microscopy imaging techniques, paving the way for future innovations in biological imaging.</p>