All advanced imagery techniques require a pinhole ‘errorless’ camera image, where geometric and chromatic distortion are removed, and without bias in the image center. In NVG and ENVG applications, it enables sub pixel edge extraction, sensor fusion, and added lossless video compression.
We discovered systematic biases in the modeling and calibration procedure for digital cameras
Our testing uses low accuracy 1024x768 and 640x480 cameras, a typical resolution for SWIR imaging. We retrieve the camera focal distance to an unmatched 10e-10 mm…
We demonstrated in June 2009 that a software image correction approach could gain
8:1 higher image accuracy
4:1 faster computation time
30% added lossless video compression
It provides us with multiple integration trade offs between, computation speed, cost, accuracy, video compression, lens selection,…
Our software platform is source code compatible and opened...
As the wavelength increases, f increases, and the image grows bigger. Measuring an object across the spectrum creates a size bias…
Lens distortion creates the biggest error in software imaging, and it amplifies getting away from the image center
As soon as you increase the lens angle of view…
Geometric distortion curves straight lines and shears objects’ squareness
Chromatic distortion splits light with respect to wavelength, whatever the spectrum
Both prevent sub pixel edge analysis and have to be removed from the image.
Some compensation comes from lens design, the remainder has to be corrected through software
Hardware design impacts on future software image enhancements
Software image correction is dependant on accurate camera calibration
is knowing how an image prints through the lens on the camera surface
For a fixed f lens
The Camera Model has three parts - External Model - Lens Model - Internal Model
5 Internal Parameters
The camera pixel being square, a should equal b, with skew parameter s close to zero
Shawn Becker’s Lens Distortion Model (MIT & NASA)
x' = x + x*(K1*r^2 + K2*r^4 + K3*r^6) + P1*(r^2 + 2*x^2) + 2*P2*x*y y' = y + y*(K1*r^2 + K2*r^4 + K3*r^6) + P2*(r^2 + 2*y^2) + 2*P1*x*y
We removed ¾ of the terms to gain accuracy!...
We have to compensate for wavelength-colour variations in order to find the true edge at sub pixel level
Leftmost data set gives results for a model equivalent to the ones generally used, and rightmost, our most accurate result using the same experimental data on our own model.
6 External Parameters
5 internal parameters
2 geometric distortion parameters
The camera pixel being square,
a should equal b=f, with skew parameter s close to zero
Left model shows error on f 10e-03mm
Right model shows error on f 10e-10mm , corrected a systematic error on image center by as much as 2 pixels, and an underestimation of distortion parameters
Amplified 50 times, our chromatic distortion model is purely radial and has a single image center for all three color channels RGB. We remove a ±½ pixel error on edge location.
Top left - red distortion, Bottom left - blue distortion
Note that they don’t peek at the same distance from the image center
Testing on a 1024x768 camera
This image was taken by a 640x480 Bayer pattern color camera using a f=4mm lens, calibrated in lab from our algorithms and setup.
Working from a 3x3 footprint on low definition cameras, edges look blockish.
Devernay’s non maxima suppression technique (INRIA 1995) works for horizontal or vertical straight lines only.
It had to be adapted for corner detection and corrected for curvature end edge orientation bias
Once corrected, it becomes a good all purpose edge detection technique for highly pixelized and blurred images
A computer generated image has exact f and perspective
Fusion of SWIR and Color images require exactly same f and exact removal of lens distortion
Fusion to synthetic image is basic to augmented reality
Color camera should have a 1024x768 resolution
Fusion with a 640x480 SWIR
Eventually use a zooming lens on the color camera…
Computation speed then becomes an issue
Synthetic vision with Vision Amplification should appear in civilian airline transportation around year 2018
SWIR wavelengths will focus further right along the lens axis…
A CCD will see lower SWIR wavelengths
Split up remaining SWIR spectrum to give spectral resolution
The most accurate Bayer pattern interpolation schemes
use edge sensing to recover missing RGB information.
Missing values are interpolated using neighbouring pixel
In a two step process, we first compute the missing G pixel values on B and R pixels
Ex.: On red pixel R13, the missing G13 value is computed as
(G12+G14)/2 if the edge is horizontal (R13>(R3+R23)/2)
(G8+G18)/2 if the edge is vertical (R13>(R11+R15)/2)
In step two, we compute missing B and R values using known G
But the lens introduces errors in the image, geometric and chromatic distortion, curving edges, and ‘color shifting’ edge location as we scan from B to G to R pixels.
The Bayer pattern recovery requires adapting for geometric and chromatic distortion, while in monochrome imaging, accuracy is dependant on optical spectrum spread.
A BAYER COLOR CAMERA IS A SPECTRUM ANALYZER
USE THE SAME SCHEME ON THE SWIR SPECTRUM ?!…