Residual Aligned: Gradient Optimization for Non-Negative Image Synthesis

TL;DR

This paper introduces a gradient optimization method called Residual Aligned for non-negative image synthesis in augmented reality, effectively preserving local lightness constancy and capturing high-frequency details in challenging scenarios.

Contribution

The proposed Residual Aligned method addresses non-negative image synthesis by preserving local lightness and high-frequency details, outperforming prior optical illusion approaches.

Findings

Strong performance in high-resolution image translation

Effective in high dynamic range scenarios

Preserves local lightness constancy

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.