In this talk, I will share a few stories of PhD research. Motivated by applications from single-molecule microscopy in biology to computational imaging in astronomy. In the first part of the talk, I will talk about the problem of non-stationary blind super-resolution, in which the point spread functions associated with point sources need to be calibrated. We propose a flexible atomic norm minimization framework to solve this daunting inverse problem. Along the way, we also derive a sample complexity bound that is optimal for this problem. The second part of the talk will discuss my summer internship project at Technicolor Research. Motivated by the business at Technicolor, I will explore the possibility of using deep neural networks for standard dynamic range (SDR) to high dynamic range (HDR) wide color conversion. Data collection, deep neural network models training and testing, as well as experimental results will be presented.