ID: 2017-062 A new mathematical function for passive auto-focusing algorithms based on Information Content (IC). The invention is a novel application of the statistical concept of entropy, used to quantitatively represent the information present in each pixel.
Principal Investigator: Matthew Linford
In all forms of passive auto-focus systems that are used in cameras, an underlying mathematical algorithm attempts to numerically describe the extent of focus or blur in the image. Accordingly, the lens system in the camera adjusts to that. This invention takes a radically different approach to the quantification of the image blur or sharpness. The underlying mathematics is novel in it’s approach and is extremely fast compared to existing algorithms, being a single step algorithm rather than a multi-step process.
In this work, a quantitative description of the image sharpness (focus) is obtained by computing the average information contained in each pixel. The figure of merit in this case is the standard deviation of the IC values of all the pixels. The value increases with an increased in sharpness of the images (when it is in focus) and vice versa. The method is better suited to low light conditions and has proven to be robust in terms of varying scenes and background. A key advantage of this method is the lack of a pre-defined threshold or selection of neighborhood. Each pixel is considered independent and the standard deviation of the IC values with change of focus is indicative of that.
Advantages of the technology include -
- Extremely fast and computationally simple method
- Doesn’t require defining any thresholds
- Doesn’t require pre-processing of the images (e.g. enhancement of image contrast)
About the Market:
This technology could be of interest to photography software manufacturers as well as camera manufacturers.
For more information, contact Dave Brown (801-422-4866)
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