Oscillatory Motion Compensation in Multi-Linear Image Sensing
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Solution Overview
Problem
Image reconstruction from multi-linear optical arrays in platforms like aircraft and satellites often results in distortions due to oscillatory motion caused by minute vibrations, leading to wavy lines in images, which existing methods fail to fully correct.
Innovation Solution
A method involving image correlation to measure relative oscillatory motion, transforming data into the frequency domain, applying inverse corrections to magnitude and phase, and converting back to time domain to generate a real-valued function representing absolute oscillatory motion, which is then applied to correct the image data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If image reconstruction is performed from multi-linear optical arrays, then complete image coverage is achieved, but oscillatory motion distortions appear in the reconstructed image
Solution Approach 1:
The patent applies preliminary action by measuring the oscillatory motion between leading and trailing arrays before the final image reconstruction. The relative oscillatory motion is captured in the time domain, transformed to the frequency domain, corrected, and then applied to compensate for distortions during reconstruction, thereby preventing distortion rather than correcting it after occurrence
Solution Approach 2:
The patent uses an intermediary approach by introducing a correction function that mediates between the raw image data from multiple arrays and the final reconstructed image. This correction function, derived from the oscillatory motion measurement, acts as an intermediate step that adjusts the image data to compensate for vibrations before final reconstruction
2Reliability
If relative oscillatory motion is measured between arrays, then motion compensation is achieved, but processing complexity increases
Solution Approach 1:
The patent replaces complex mechanical vibration compensation systems with a signal processing approach. Instead of physically stabilizing the imaging arrays or using complex mechanical corrections, the system measures oscillatory motion through image correlation, transforms the data to the frequency domain, applies mathematical corrections, and reconstructs the image, thereby substituting mechanical complexity with computational elegance
Solution Approach 2:
The patent applies parameter changes by transforming the oscillatory motion data from the time domain to the frequency domain using Fast Fourier Transform. This parameter transformation allows for more effective analysis and correction of the oscillatory patterns, as frequency-domain operations simplify the correction process compared to time-domain manipulation
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
This approach effectively compensates for absolute oscillatory motion, reducing residual distortions and improving image quality by accurately accounting for high-frequency vibrations.
Implementation Method 1
measuring a relative oscillatory motion from a first-imaged array of the multi-linear optical array to a second-imaged array of the multi-linear optical array as a first function in time domain via image correlation
Implementation Method 2
The transforming of the first function to the second function may include applying a fast fourier transform (FFT).
Data Source
AI summary
A system and a method for processing multi-linear image data by measuring a relative oscillatory motion from a first-imaged array of the multi-linear optical array to a second-imaged array of the multi-linear optical array as a first function in time domain via image correlation; transforming the first function from the time domain to a second function in frequency domain; converting real and the imaginary parts of the second function to polar coordinates to generate a magnitude and a phase; correcting the polar coordinates from the second function in the frequency domain to generate a third function; converting the third function to rectangular coordinates to generate a fourth function in the frequency domain; and transforming the fourth function from the frequency domain to a fifth function in the time domain.