The benefits of deep learning (DL) based denoising of MR images include reduced acquisition time and improved image quality at low field strength. However, simulating noisy images require biophysical models that are field and acquisition dependent. Scaling these simulations is complex and computationally intensive. In this work, we instead leverage the native noise of the data, dubbed “native noise denoising network” (NNDnet). We applied NNDnet to three different MR data types and computed the peak signal-to-noise ratio (> 38dB) for training performance and image entropy (> 4.25) for testing performance in the absence of a reference image.