There are quite a number of photographs captured under undesirable conditions in the last century. Thus, they are often noisy, regionally incomplete, and grayscale formatted. Conventional approaches mainly focus on one point so that those restoration results are not perceptually sharp or clean enough. To solve these problems, we propose a noise prior learner NEGAN to simulate the noise distribution of real legacy photos using unpaired images. It mainly focuses on matching high-frequency parts of noisy images through discrete wavelet transform (DWT) since they include most of noise statistics. We also create a large legacy photo dataset for learning noise prior. Using learned noise prior, we can easily build valid training pairs by degrading clean images. Then, we propose an IEGAN framework performing image editing including joint denoising, inpainting and colorization based on the estimated noise prior. We evaluate the proposed system and compare it with state-of-the-art image enhancement methods. The experimental results demonstrate that it achieves the best perceptual quality. Please see the webpage \href{https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior}{https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior} for the codes and the proposed LP dataset.
翻译:在上个世纪,在不可取的条件下拍摄了相当多的照片,因此,这些照片往往是噪音、区域不完整和灰度格式化的。常规方法主要侧重于一个点,这样恢复结果就不会在感知上明显或足够干净。为了解决这些问题,我们提议在前学习者NEGAN发出噪音,以模拟真实遗迹照片的噪音分布,它主要侧重于通过离散波盘变换(DWT)对噪音图像的高频部分进行匹配,因为它们包含大部分噪音统计数据。我们还创建了一个大型的遗留照片数据集,用于在之前学习噪音。在之前使用已学的噪音,我们可以很容易地通过降低清洁图像来建立有效的培训配对。然后,我们提议建立一个IEGAN框架,根据以前估计的噪音进行图像编辑,包括联合拆卸、油漆和彩色化。我们评价了拟议的系统,并将其与最新艺术图像增强方法进行比较。实验结果显示,它达到了最佳的概念质量。请参见网页:https://github.com-aruzhi/Leghi-Lizouy-GIS-Nement-Laghius-GIS-Editing。