Kinetic Super-Resolution Imaging via Passive Registration
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Solution Overview
Problem
Existing super-resolution algorithms face challenges in achieving stable and efficient image reconstruction from motion data due to the ill-posed nature of the inverse problem and reliance on external measures for camera position, which are prone to drift errors.
Innovation Solution
A novel method that characterizes camera motion directly from image sequences using passive registration and kinetic point spread function calculation to derive a high-resolution image, allowing for the application of motion-based super-resolution techniques in conjunction with traditional methods.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If external measures such as accelerometers and tilt meters are used to track camera position for super-resolution imaging, then motion data can be obtained, but drift errors accumulate and reduce measurement precision
Solution Approach 1:
The system uses the image data itself to determine camera motion through passive registration, making the system self-sufficient without external measurement devices. The images serve dual purposes: as the object being processed and as the reference for determining motion, eliminating drift errors inherent in external sensors.
Solution Approach 2:
The patent replaces mechanical sensing systems (accelerometers, tilt meters) with an optical/image processing-based system. Instead of using physical sensors to measure camera motion, the system uses computational image registration to derive motion parameters directly from the image sequences.
2Manufacturing precision
If blind deconvolution is performed to reconstruct super-resolution images, then both the high-resolution image and PSF are approximated simultaneously, but the inverse problem becomes ill-posed and computationally unstable
Solution Approach 1:
The patent performs preliminary action by determining camera motion and characterizing the PSF before performing the deconvolution. By using passive registration to establish accurate motion parameters first, the system prepares the necessary information in advance, making the subsequent deconvolution process more stable and less computationally intensive than blind deconvolution.
Data Source
AI summary
Methods and a computer program product for deriving a super-resolution image of a physical object, the super-resolution image characterized by a resolution exceeding a “camera imaging resolution” associated with each of a sequence of lower-resolution images of the physical object. The sequence of images of the physical object is obtained at a plurality of relative displacements with respect to the object. An offset is passively associated with each of the plurality of images to derive effective camera movement, allowing for calculation of a kinetic point spread function on the basis of the effective camera movement. The image sequence is deconvolved, using the kinetic point spread function, to solve for a high-resolution image. Various applications such as portable cameras and infrared imaging for energy conservation are described.