A self-validation Noise2Noise training framework for image denoising

Image denoising is a crucial algorithm in image processing that aims to enhance image quality. Deep learning-based image denoising methods can be categorized into supervised and unsupervised approaches. Supervised learning requires pairs of noisy and noise-free training data, which is impractical in real-world scenarios. Unsupervised learning uses pairs of noisy images for training, but it may yield lower accuracy. Additionally, deep learning-based methods often require a large amount of training data. To overcome these challenges, this research proposes a self-validation Noise2Noise (SV-N2N) framework that generates validation sets using only noisy images without requiring noise-free pairs. The proposed SV-N2N method effectively reduces noise, comparable to supervised and unsupervised methods, without requiring a noise-free ground truth, which is efficient for solving real-world scenarios.