This project looks at solutions for removing noise from rendered images in real-time. Rendering is the process of creating an image from a model of a scene or environment. Creating detailed renders can be an extremely costly process, so a recent push in graphics has been to create noisy renders and then estimate the detailed output. Given a rendered image, how can we quickly denoise this result? Fast denoising would be useful when a game company wants to denoise the scenes in their game in real-time, or when an animation studio needs quick results to see how a scene will look in their final animation. This project focuses on multiple concepts that can help machine learning models approach denoising in real-time. The main novel concept the project introduces is an optimization to allocate computation based on region detail. We use a chain of multiple Convolutional Neural Networks (CNNs) to estimate a detailed image at different levels of refinement. After each refinement a binary-mask estimator is introduced to decide which regions have reached sufficient levels of quality. The model then continues on more complicated image regions. In my talk I will detail the model's results and give a brief survey on the current state of the art.