论文标题
从还原还原：单图像使用伪干净的图像降级
Restore from Restored: Single Image Denoising with Pseudo Clean Image
论文作者
论文摘要
在这项研究中，我们提出了一种称为“恢复 - 恢复的”的简单有效的微调算法，该算法可以极大地提高全面训练的图像deoing网络的性能。许多有监督的denoising方法可以使用大型外部训练数据集产生令人满意的结果。但是，这些方法在使用给定的测试图像中使用的内部信息时有局限性。相比之下，最近的自我监督方法可以通过利用特定的测试输入中的信息来消除输入图像中的噪声。但是，与监督方法相比，这种方法在已知噪声类型（例如高斯噪声）上的性能相对较低。因此，为了结合外部信息和内部信息，我们使用测试时使用伪训练来微调全面训练的DeNoiser。通过利用内部自相似补丁（即补丁复发），可以将基线网络改编为给定的特定输入图像。我们证明，我们的方法可以轻松地在最先进的Denoising网络之上，并进一步改善了包括真实嘈杂图像在内的众多DeNoising基准数据集的性能。
In this study, we propose a simple and effective fine-tuning algorithm called "restore-from-restored", which can greatly enhance the performance of fully pre-trained image denoising networks. Many supervised denoising approaches can produce satisfactory results using large external training datasets. However, these methods have limitations in using internal information available in a given test image. By contrast, recent self-supervised approaches can remove noise in the input image by utilizing information from the specific test input. However, such methods show relatively lower performance on known noise types such as Gaussian noise compared to supervised methods. Thus, to combine external and internal information, we fine-tune the fully pre-trained denoiser using pseudo training set at test time. By exploiting internal self-similar patches (i.e., patch-recurrence), the baseline network can be adapted to the given specific input image. We demonstrate that our method can be easily employed on top of the state-of-the-art denoising networks and further improve the performance on numerous denoising benchmark datasets including real noisy images.
