Learning image blind denoisers without explicit noise modeling

Image blind denoising aims at removing the unknown noise from given images to improve the image’s visual quality. Current blind denoisers can be categorized into two classes, i.e., traditional and deep learning-based methods. Generally, deep learning-based methods are likely to outperform the traditional methods when the model of image noise is within the training noise distribution. However, when the image noise is unseen during the optimization process, traditional methods usually achieve better denoising performance. To address the generalization problem of deep learning-based denoiser, our paper proposed a general yet concise image blind denoising framework that can be applied to existing deep learning-based denoisers. Specifically, given noisy images, our method first extracts high-frequency signals from the images and synthesizes noise from the extracted signals using several noise generation techniques. In contrast to the existing conventional approaches that explicitly model the noise, our proposed framework advances in generating noise directly from the noisy images without explicitly noise modeling. By applying the generated noise to clean images to construct noisy-clean paired images, the deep learning-based denoisers can be optimized using the constructed data with the similar noise of the given noisy images. After optimization, the denoisers are capable of removing noise from the given noisy images. Extensive experiments and comprehensive ablations on both synthetic noisy image and real-world image denoising tasks demonstrate the superiority of our proposed framework over all the compared approaches.