Abstract
Deep learning based image denoising methods have been recently popular due to their improved performance. Traditionally, these methods are trained in a supervised manner, requiring a set of noisy input and clean target image pairs. More recently, self-supervised approaches have been proposed to learn denoising from noisy images only, without requiring clean ground truth during training. Succinctly, these methods assume that an image pixel is correlated with its neighboring pixels, while the noise is independent. In this work, building on these approaches and recent methods from image reconstruction, we introduce Noise2Inpaint (N2I), a training approach that recasts the denoising problem into a regularized image inpainting framework. This allows us to use an objective function, which can incorporate different statistical properties of the noise as needed. We use algorithm unrolling to unroll an iterative optimization for solving this objective function and train the unrolled network end-to-end. The training is self-supervised without requiring clean target images, where pixels in the noisy image are split into two disjoint sets. One of these is used to impose data fidelity in the unrolled network, while the other one defines the loss. We demonstrate that N2I performs successful denoising on real-world datasets, while preserving better details compared to its self-supervised counterpart Noise2Void.
Abstract (translated)
URL
https://arxiv.org/abs/2006.09450
