Wide dynamic range fusion using memory look-up

Details Wide dynamic range fusion using memory look-up Wide dynamic range fusion using memory look-up

1999-04-23

2003-06-24

Mehta, Bhavesh (Department: 2625)

Image analysis

Image transformation or preprocessing

Combining image portions

C382S295000, C382S294000, C382S293000, C345S630000, C345S629000, C348S588000, C348S584000

Reexamination Certificate

active

06584235

ABSTRACT:

The present system teaches a way of carrying out fusion between images based on the specific content of the images.
BACKGROUND
Once a image is sensed, that image can be processed to obtain better information, i.e., information that is more pleasing to the eye. Many different image processing techniques and algorithms are well known. One known image processing technique is called image fusion. An example of image fusion is described in U.S. Pat. No. 5,488,674.
Image fusion combines two or more source images to form a single composite image with extended information content. Images from different sensors, such as infra-red and visible cameras, computer aided tomography (CAT) and magnetic resonance imaging (MRI) systems, can be, for example, combined to form the composite image. Multiple images of a given scene taken with different types of sensors, such as visible and infra-red cameras, or images taken with a given type of sensor and scene but under different imaging conditions, such as with different scene illumination or camera focus may be combined. Image fusion can be used to obtain useful information from the source images and to attempt to remove artifacts generated by the fusion process.
Different approaches are known to carry out image fusion. One approach aligns the source images, then sums, or averages, across images at each pixel position. This and other pixel-based approaches often field unsatisfactory results since individual source features appear in the composite with reduced contrast, or appear jumbled as in a photographic double exposure.
Known pattern-selective image fusion tries to overcome these deficiencies by identifying salient features in the source images and preserving these features in the composite at full contrast. Each source image is first decomposed into a set of primitive pattern elements. A set of pattern elements for the composite image is then assembled by selecting salient patterns from the primitive pattern elements of the source images. The composite image is constructed from its set of primitive pattern elements.
Burt, in Multiresolution Image Processing And Analysis, V. 16, pages 20-51, 1981, and Anderson, et al in U.S. Pat. No. 4,692,806 disclose an image decomposition technique in which an original comparatively-high-resolution image comprised of a first number of pixels is processed to derive a wide field-of-view, low resolution image comprised of second number of pixels smaller than the first given number. The process for decomposing the image to produce lower resolution images is typically performed using a plurality of low-pass filters of differing bandwidth having a Gaussian roll-off. U.S. Pat. No. 4,703,514, for example, has disclosed a means for implementing the pyramid process for the analysis of images.
While the Laplacian Pyramid technique has been found to provide good results, it too has some problems at times. Sometimes, for example, visible artifacts are introduced into the composite image.
SUMMARY
The present system uses a reconfigurable system that carries out the image fusion using an adaptable technique.
Specifically, a look up table is used to obtain a predetermined image analysis. The look up table is preferably formed by making a predetermined image relationship between different parts, storing that in the look up table, and addressing those using the pixel values used to form the relationship.


REFERENCES:
patent: 5325449 (1994-06-01), Burt et al.
patent: 5488674 (1996-01-01), Burt et al.
patent: 5511155 (1996-04-01), Yamaguchi
patent: 5828793 (1998-10-01), Mann
patent: 5886822 (1999-03-01), Spitzer
patent: 6075905 (2000-06-01), Herman et al.
Eric R. Fossum, “Differential Non-Linearity Correction Scheme”, U.S. patent application No. 09/170,944 filed Oct. 13, 1998.
Alexander I. Krymski, “Correction of Missing Codes Nonlinearity in A to D Converters”, U.S. patent application No. 09/169,020 filed Oct. 8, 1998.
Michael Kaplinsky, “Using Single Lookup Table to Correct Differential Non-linearity Errors in an Array of A/D Converters”, U.S. patent application No. 09/304,526 filed May 5, 1999.
Eric R. Fossum, Sandor Barna, Daniel Van Blerkom, “Nonlinear Flash Analog to Digital Converter Used in an Active Pixel System”, U.S. patent application No. 09/161,355 filed Sep. 25, 1998.

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