Image enhancement via iterative refinement based on machine learning models

A computer-implemented method, comprising:

• receiving, by a computing device, an input image;
• applying a neural network to predict an enhanced version of the input image by iteratively denoising the input image, wherein the iterative denoising is based on a reverse Markov chain associated with a forward Gaussian diffusion process, the neural network having been trained by applying the forward Gaussian diffusion process to add Gaussian noise to at least one corresponding target version of each image of a plurality of pairs of images in training data;
• and
• outputting the predicted enhanced version of the input image.

A computing device, comprising:

• one or more processors;
• and
• data storage, wherein the data storage has stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computing device to carry out operations comprising:
• receiving, by the computing device, an input image;
• applying a neural network to predict an enhanced version of the input image by iteratively denoising the input image, wherein the iterative denoising is based on a reverse Markov chain associated with a forward Gaussian diffusion process, the neural network having been trained by applying the forward Gaussian diffusion process to add Gaussian noise to at least one corresponding target version of each image of a plurality of pairs of images in training data;
• and
• outputting the predicted enhanced version of the input image.