GENERATIVE RECORRUPTED-TO-RECORRUPTED: AN UNSUPERVISED IMAGE DENOISING NETWORK FOR ARBITRARY NOISE DISTRIBUTION Anonymous

Abstract

With the great breakthrough of supervised learning in the field of denoising, more and more works focus on end-to-end learning to train denoisers. The premise of this method is effective is that there is certain data support, but in practice, it is particularly difficult to obtain labels in the training data. To this end, some unsupervised denoisers have emerged in recent years, however, the premise of these methods being effective is that the noise model needs to be known in advance, which will limit the practical use of unsupervised denoising. In addition, inaccurate noise prior from noise estimation algorithms causes low denoising accuracy. Therefore, we design a more practical denoiser that requires neither clean images as training labels nor noise model assumptions. Our method also needs the support of the noise model, the difference is that the model is generated by a residual image and a random mask during the network training process, and then the input and target of the network are generated from a single noisy images and the noise model, at the same time, train an unsupervised module and a pseudo supervised module. Extensive experiments demonstrate the effectiveness of our framework and even surpass the accuracy of supervised denoising.

1. INTRODUCTION

Image denoising is a traditional topic in the field of image processing, and it is the basis for the success of other vision tasks. A noisy image can be represented by y = x + n, and our task is to design a denoiser to remove the noise in the noisy image. The denoising convolutional neural network DNCNN Zhang et al. (2017) can be considered a benchmark of the use of deep learning for image denoising, and it introduced residual learning and batch normalization, which speed up the training process as well as boost the denoising performance. The fast and lexible denoising neural network FFDNET Zhang K. & Zhang (2018) treated the noisy model as a prior probability distribution, such that it can effectively handles wide a range of noise levels The convolutional blind denoising CBDNET Guo et al. (2019) went further than the FFDNET Zhang K. & Zhang ( 2018) and aimed at real photographs though synthesized and real images were both used in training. A common treatment for the above methods is that it all need to take noisy-clean image pairs in training. However, in some scenarios such as medical and biological imaging, there often lack clean images, leading to an infeasibility of the above methods. To this end, the noise-to-noise(N2N) method Lehtinen et al. (2018) was the first to reveal that deep neural networks (DNNs) can be trained with pairs of noisy-noisy images instead of noisy-clean images, in other words, training can be conducted with only two noisy images that are captured independently in the same scene. The N2N can be used in many taskBuchholz et al. ( 2019 2021) concluded that it is still possible to train the network without using clean images if the noise between each region of the image is independent. Among them,



); Ehret et al. (2019); Hariharan et al. (2019); Wu et al. (2019); Zhang et al. (2019), since it creatively addressed the dependency on clean images. Unfortunately, pairs of corrupted images are still difficult to be obtained in dynamic scenes with deformations and image quality variations. To further bring the N2N into practice, some research Krull et al. (2019); Batson & Royer (2019); Krull et al. (2020); Huang et al. (

