Super-Resolved Depth Mapping From Low-Resolution Image Frames
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Solution Overview
Problem
Aerial images and videos captured by low-resolution image sensors often result in poor depth information and 3D representations due to their low image quality, which is inadequate for applications requiring high-resolution images, such as computer vision and mixed-reality experiences.
Innovation Solution
A system that groups low-resolution image frames based on pose similarity and applies super-resolution processing to generate high-resolution composite images, which are then used for depth processing techniques like stereo matching to produce accurate depth maps.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If low-resolution image sensors are used for aerial imaging, then storage and bandwidth requirements are reduced, but depth information quality deteriorates
Solution Approach 1:
The patent combines multiple low-resolution images into a single high-resolution composite image by merging corresponding pixels from multiple input images. This merging process reconstructs fine details that were lost in individual low-resolution images, enabling high-quality depth information extraction while maintaining the advantage of using low-resolution sensors for reduced storage and bandwidth requirements.
2Measurement precision
If high-resolution image sensors are used for aerial imaging, then depth information quality is improved, but storage and bandwidth requirements increase
Solution Approach 1:
The patent creates a high-resolution copy of the scene by synthesizing a composite image from multiple low-resolution images. Instead of capturing high-resolution images directly (which would require high-resolution sensors and generate large data volumes), the system copies and processes multiple low-resolution images to reconstruct high-resolution detail, thereby achieving high depth information quality without the storage and bandwidth burden of actual high-resolution captures.
3Measurement precision
If multiple low-resolution images are processed to generate high-resolution composite images, then depth information quality is improved, but processing complexity increases
Solution Approach 1:
The patent segments the image processing task into distinct stages: capturing multiple low-resolution images, aligning them based on pose information, combining corresponding pixels to generate the composite image, and finally extracting depth information. This segmentation of the processing workflow manages complexity by breaking down the overall task into manageable, sequential operations rather than attempting to perform all processing in a single complex step.
Data Source
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AI summary
A system for generating depth information from low-resolution images is configured to access a plurality of image frames capturing an environment, identify a first group of image frames from the plurality of image frames, and generate a first image comprising a first composite image of the environment using the first group of image frames as input. The first composite image has an image resolution that is higher than an image resolution of the image frames of the first group of image frames. The system is also configured to obtain a second image of the environment, where parallax exists between a capture perspective associated with the first image and a capture perspective associated with the second image. The system is also configured to generate depth information for the environment based on the first image and the second image.