论文标题
残留对准:非负图像合成的梯度优化
Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis
论文作者
论文摘要
在这项工作中,我们解决了光学的重要问题,请参见(OST)增强现实:非负图像合成。大多数图像生成方法在这种情况下失败,因为它们可以完全控制每个像素,并且无法通过添加光创建较暗的像素。为了解决AR图像合成中的非负图像产生问题,先前的作品试图利用光学幻觉来模拟人类的视觉,但在诸如高动态范围之类的情况下无法很好地保留轻度稳定性。在我们的论文中,我们提出了一种能够在地方一级保存亮度恒定的方法,从而捕获高频细节。与现有工作相比,我们的方法在图像到图像翻译任务中表现出很强的性能,尤其是在大规模图像,高分辨率图像和高动态范围图像传输等方案中。
In this work, we address an important problem of optical see through (OST) augmented reality: non-negative image synthesis. Most of the image generation methods fail under this condition, since they assume full control over each pixel and cannot create darker pixels by adding light. In order to solve the non-negative image generation problem in AR image synthesis, prior works have attempted to utilize optical illusion to simulate human vision but fail to preserve lightness constancy well under situations such as high dynamic range. In our paper, we instead propose a method that is able to preserve lightness constancy at a local level, thus capturing high frequency details. Compared with existing work, our method shows strong performance in image-to-image translation tasks, particularly in scenarios such as large scale images, high resolution images, and high dynamic range image transfer.