<p><br></p><p dir="ltr">Achieving high optical resolution imaging is one of the most important goals in the history of optics. However, due to finite aperture sizes, a diffraction limit is imposed on optical imaging. Therefore, the Rayleigh limit, which describes the minimum separation at which two point sources are resolvable, has served as a critical limit in optical resolution. Many methods have been studied to break the limit and succeed in resolving nearby sources below the Rayleigh criterion but only beyond a certain distance. Furthermore, it has been demonstrated that quantum-inspired optics techniques maintain consistent variance in estimating the separation of point sources even at low separations, but only with prior information like a known number of sources and equal brightness. Therefore, achieving the ultimate optical resolution remains an open question. This thesis will conclusively address this challenge considering real-world scenarios, i.e., no prior information or controlled lab environment as well as low signal-to-noise ratio (SNR), turbulence, and other practical challenges.</p><p><br></p><p dir="ltr">In information theory, the estimation variance of a random parameter can be quantified using the inverse of Fisher information. By maximizing the Fisher information, one can minimize the variance in estimation. In my thesis, we have shown that the measurement can be accelerated without sacrificing optical resolution using the adaptive mode so that quantum Fisher information per detected photon is maximized. The notable attribute that sets it apart from other quantum-inspired methods is that it does not require any prior information, making it more feasible for practical application. We have further shown that the space domain awareness (SDA) challenge can be effectively handled with the aforementioned approach with a very limited photon budget and even in the presence of turbulence. Toward solving the challenges, we designed a photon statistics-based direct imaging method that can also serve as a baseline method for quantum optics. In my thesis, atmospheric turbulence is also deeply explored and the effect is mitigated using reinforcement learning.</p><p><br></p>