Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis
This work addresses a critical challenge in AR image synthesis for applications requiring realistic visual overlays, though it appears incremental by improving on prior methods for a specific bottleneck.
The paper tackles the problem of non-negative image synthesis for optical see-through augmented reality, where existing methods fail to preserve lightness constancy, and proposes a gradient optimization method that achieves strong performance in image-to-image translation tasks, particularly for large-scale, high-resolution, and high dynamic range images.
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.