Self-Augmented Noisy Image for Noise2Noise Image Denoising

Image denoising is a critical task in image processing aimed at removing noise artifacts. Typically, supervised deep learning often necessitates a large number of pairs of noisy and noise-free images for training. Noise2Noise techniques have demonstrated efficiency in noise removal without relying on a noise-free ground truth. This is achieved through a learning process that approaches input to target points, balancing results across all training inputs. While Noise2Noise can be adapted for single image denoising, it still faces challenges in single image and blind noise scenarios. To address this issue, our research introduces the concept of self-augmented noisy images for self-supervised Noise2Noise single image denoising. The proposed method leverages the behavior of the training process, which strives to balance the loss values appropriately for each training set. By utilizing the same noisy image for both input and validation to learn self-identification, it produces another set of noisy images that mimic the input noisy images. From the experimental results, measured using Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index Measure (SSIM) metrics, it is evident that the proposed self-augmented strategy enables Noise2Noise to remove noise in single image scenarios. Additionally, it achieves performance comparable to other unsupervised denoising methods without requiring additional augmentation manipulations.