Search Results for author:
Found 5 papers, 1 papers with code
Noise-based Enhancement for Foveated Rendering
Novel image synthesis techniques, so-called foveated rendering, exploit this observation and reduce the spatial resolution of synthesized images for the periphery, avoiding the synthesis of high-spatial-frequency details that are costly to generate but not perceived by a viewer.
Closed-Loop Control of Additive Manufacturing via Reinforcement Learning
We further show that in combination with reinforcement learning, our model can be used to discover control policies that outperform state-of-the-art controllers.
Learning GAN-based Foveated Reconstruction to Recover Perceptually Important Image Features
Given the nature of GAN-based solutions, we focus on the sensitivity of human vision to hallucination in case of input samples with different densities.
Why Are Deep Representations Good Perceptual Quality Features?
In particular, we focus our analysis on fundamental aspects of human perception, such as the contrast sensitivity and orientation selectivity.
Towards a quality metric for dense light fields
We find that the existing image quality metrics provide good measures of light-field quality, but require dense reference light- fields for optimal performance.
