Dual Path Unsupervised Real Image Denoising

Because of the significant gap between the real noise and the synthetic noise domains, the learning-based method trained on the synthetic noise performs poorly on the task of removing the real noise. To solve the problem, the dual path unsupervised real image denoising method is proposed, by using the unsupervised path of the image translation between the real noise domain and the synthetic noise domain and the indirect supervised path of the image denoising from the realistic noisy images to the noisy-free images. The image translation between the real noise domain and the synthetic noise domain augments the training images, and the effective supervised denoising model is used to tackle with the unsupervised real image denoising problem. This method network structure is flexible to adjust the different tasks in the practical applications. Compared to other unsupervised real image denoising methods, our method can adaptively generate the synthetic noise that shares a similar distribution as the real noise. Denoising experiments on real-world sRGB images show the effectiveness of the proposed method.