Google llc (20240265490). Machine Learning Models for Image Interpolation simplified abstract
Contents
Machine Learning Models for Image Interpolation
Organization Name
Inventor(s)
Janne Matias Kontkanen of San Francisco CA (US)
Eric Tabellion of Belmont CA (US)
Brian Lee Curless of Seattle WA (US)
Fitsum Reda of Santa Clara CA (US)
Caroline Rebecca Pantofaru of San Carlos CA (US)
Machine Learning Models for Image Interpolation - A simplified explanation of the abstract
This abstract first appeared for US patent application 20240265490 titled 'Machine Learning Models for Image Interpolation
The abstract describes a computer system with processors and computer-readable media storing a machine-learned image interpolation model.
- The machine-learned model extracts feature values from input images at different scales.
- It generates flow estimates for each pair of input images, indicating flow from interpolation time to capture time.
- The model warps feature values based on flow estimates to create warped sets of features.
- An interpolated image is generated using the warped sets of features and different scales.
Potential Applications: - Image processing and enhancement in photography and video editing. - Medical imaging for enhancing resolution and clarity. - Satellite imagery for improved analysis and visualization.
Problems Solved: - Enhances image quality and resolution. - Provides a more accurate representation of data. - Improves visual analysis in various fields.
Benefits: - Enhanced image quality and resolution. - Improved accuracy in data representation. - Enhanced visual analysis capabilities.
Commercial Applications: Title: "Advanced Image Interpolation Technology for Enhanced Visual Analysis" This technology can be used in industries such as photography, medical imaging, satellite imagery, and video production to improve image quality and enhance visual analysis capabilities.
Prior Art: Researchers can explore existing patents related to image interpolation, machine learning in image processing, and computer vision technologies to understand the prior art in this field.
Frequently Updated Research: Researchers can stay updated on advancements in machine learning algorithms for image processing, computer vision techniques, and applications of image interpolation in various industries.
Questions about Image Interpolation Technology: 1. How does image interpolation technology impact the field of medical imaging? 2. What are the potential limitations of using machine-learned models for image interpolation?
Original Abstract Submitted
provided is a computer system that includes one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned image interpolation model. the machine-learned image interpolation model is configured to: extract, for each of multiple different scales, a respective set of feature values from each of a pair of input images; generate, for each of the multiple different scales, a respective flow estimate for each of the pair of input images that indicates a respective flow from the interpolation time to the respective capture time; warp, for each of the multiple different scales, the respective set of feature values for each of the pair of input images according to the respective flow estimate to generate respective warped sets of features; and generate a interpolated image based on the respective warped sets of features for the pair of input images and the multiple different scales.
