论文标题
噪音破坏者：逐渐的图像在噪声分析的指导下降级
NoiseBreaker: Gradual Image Denoising Guided by Noise Analysis
论文作者
论文摘要
完全监督的基于深度学习的DeNoiser目前是最具性能的图像Denoising解决方案。但是，它们需要干净的参考图像。当目标噪声复杂时，例如由未知强度的主要噪声的未知混合物组成，完全监督的解决方案受到难以为问题建立合适的训练集的困难。本文提出了一种逐步的Denoising策略，该策略迭代地检测图像中的主导噪声，并使用量身定制的DeNoiser将其除去。该方法显示出与混合噪声上的最先进的盲人Denoisers的同步。此外，噪声分析被证明可有效地指导DeNoiser，不仅是噪声类型，而且对噪声强度。该方法提供了对遇到的噪声的性质的见解，并且可以扩展具有新的噪声性质的现有DeNoiser。此功能使该方法适应了各种剥离案例。
Fully supervised deep-learning based denoisers are currently the most performing image denoising solutions. However, they require clean reference images. When the target noise is complex, e.g. composed of an unknown mixture of primary noises with unknown intensity, fully supervised solutions are limited by the difficulty to build a suited training set for the problem. This paper proposes a gradual denoising strategy that iteratively detects the dominating noise in an image, and removes it using a tailored denoiser. The method is shown to keep up with state of the art blind denoisers on mixture noises. Moreover, noise analysis is demonstrated to guide denoisers efficiently not only on noise type, but also on noise intensity. The method provides an insight on the nature of the encountered noise, and it makes it possible to extend an existing denoiser with new noise nature. This feature makes the method adaptive to varied denoising cases.
