Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis
- URL: http://arxiv.org/abs/2202.04036v1
- Date: Tue, 8 Feb 2022 18:04:32 GMT
- Title: Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis
- Authors: Flora Yu Shen, Katie Luo, Guandao Yang, Harald Haraldsson, Serge
Belongie
- Abstract summary: We 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.
- Score: 11.026337830218067
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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.
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