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arXiv 2202.04036cs.CV
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残差对齐:非负像合成的梯度优化

Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis

Cornell Tech(康奈尔科技学院);Cornell University(康奈尔大学)

Flora Yu Shen, Katie Luo, Guandao Yang, Harald Haraldsson, Serge Belongie

摘要:

在这项工作中,我们解决了一个重要的问题,光学透视(OST)增强现实:非负像合成。大多数图像生成方法在这种情况下都失败了,因为它们假设对每个像素都有完全的控制,并且不能通过添加光线来创建更暗的像素。为了解决AR图像合成中的非负像生成问题,现有技术已经尝试利用光学错觉来模拟人类视觉,但是在诸如高动态范围的情况下未能很好地保持亮度恒定性。在我们的论文中,我们提出了一种能够在局部水平上保持亮度恒定性的方法,从而捕获高频细节。与现有的工作相比,我们的方法在图像到图像的翻译任务,特别是在场景,如大规模图像,高分辨率图像,高动态范围的图像传输表现出强大的性能。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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