Noise2Noise is a method for image restoration that learns to restore images using only corrupted examples, without requiring clean data or explicit statistical models of the corruption. The method trains a neural network to map corrupted inputs to clean outputs by minimizing the empirical risk based on noisy data. This approach has been shown to perform as well as or better than traditional methods that use clean data, and it is applicable to various tasks such as denoising, super-resolution, and MRI reconstruction. The method is based on statistical reasoning, where the goal is to minimize the expected deviation from the corrupted observations. This is achieved by training a regression model, such as a convolutional neural network (CNN), using pairs of corrupted inputs and clean targets. The loss function used in the training process is minimized to find the best mapping from corrupted inputs to clean outputs. The method has been tested on various types of noise, including Gaussian, Poisson, and Bernoulli noise, and has shown promising results in denoising and other image restoration tasks. It has also been applied to MRI reconstruction, where it has demonstrated the ability to recover images from undersampled data. The method is particularly effective in scenarios where obtaining clean data is difficult or impractical. It does not require an explicit statistical model of the corruption or an image prior, and instead learns these indirectly from the training data. This makes it a powerful tool for image restoration in a wide range of applications.Noise2Noise is a method for image restoration that learns to restore images using only corrupted examples, without requiring clean data or explicit statistical models of the corruption. The method trains a neural network to map corrupted inputs to clean outputs by minimizing the empirical risk based on noisy data. This approach has been shown to perform as well as or better than traditional methods that use clean data, and it is applicable to various tasks such as denoising, super-resolution, and MRI reconstruction. The method is based on statistical reasoning, where the goal is to minimize the expected deviation from the corrupted observations. This is achieved by training a regression model, such as a convolutional neural network (CNN), using pairs of corrupted inputs and clean targets. The loss function used in the training process is minimized to find the best mapping from corrupted inputs to clean outputs. The method has been tested on various types of noise, including Gaussian, Poisson, and Bernoulli noise, and has shown promising results in denoising and other image restoration tasks. It has also been applied to MRI reconstruction, where it has demonstrated the ability to recover images from undersampled data. The method is particularly effective in scenarios where obtaining clean data is difficult or impractical. It does not require an explicit statistical model of the corruption or an image prior, and instead learns these indirectly from the training data. This makes it a powerful tool for image restoration in a wide range of applications.