论文标题
遗产照片编辑与博学的噪音先验
Legacy Photo Editing with Learned Noise Prior
论文作者
论文摘要
在上个世纪，在不良条件下捕获了许多照片。因此，它们通常是嘈杂的，区域不完整的，灰度格式。传统方法主要集中在一个点上，因此这些恢复结果在感知上不够清晰或清洁。为了解决这些问题，我们提出了噪音先验的学习者Negan，以模拟使用未配对的图像的真实遗产照片的噪声分布。它主要集中于通过离散小波变换（DWT）匹配嘈杂图像的高频部分，因为它们包括大多数噪声统计数据。我们还创建了一个大型旧的照片数据集，以便先验学习噪音。在先验之前，我们可以通过降低干净的图像来轻松构建有效的训练对。然后，我们提出了一个执行图像编辑的IEGAN框架，包括基于估计的噪声先验的关节去胶，介绍和着色。我们评估了提出的系统，并将其与最先进的图像增强方法进行比较。实验结果表明，它达到了最佳的感知质量。 https://github.com/zhaoyuzhi/legacy-photo-editing------------- learned-noise-prior for代码和拟议的LP数据集。
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. https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior for the codes and the proposed LP dataset.
