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Auto-focusing algorithm using discrete wavelet transform
| Details |
Inventors: Acharya, Tinku; Metz, Werner;
Assignee: Intel Corporation (Santa Clara, CA)
Primary Examiner: Lee; Thomas D.
Assistant Examiner: Brinich; Stephen
Attorney, Agent or Firm: Blakely, Sokoloff, Taylor & Zafman LLP
What is disclosed is a method that includes generating an overall sharpness parameter for a given focus position by performing a Discrete Wavelet Transform upon an image captured by an imaging device and automatically focusing said imaging device to an optimal focus position by comparing a plurality of different sharpness parameters. The optimal focusing position is automatically determined by finding the highest sharpness parameter for a given scene. |
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DETAILED DESCRIPTION OF THE INVENTION The exemplary embodiments described herein are provided merely to illustrate the principles of the invention and should not be construed as limiting the scope of the invention.
Rather, the principles of the invention may be applied to a wide range of systems to achieve the advantages described herein and to achieve other advantages or to satisfy other objectives as well.
Using traditional Fourier analysis or transforms, any signal can be approximated as a sum of sinusoidal waveforms of assorted frequencies.
While Fourier transforms are ideally suited for signals having repeated behavior, they fail to efficiently approximate signals with sharp discontinuities such as the edge features in images, or signals encoded for digital communications.
Thus, Fourier analysis is unsuited where edge features need to be detected in order to determine sharpness since the Fourier transform does not, in the first place, represent edge features adequately.
Another form of signal analysis, known as Wavelet analysis has been developed to better represent signals that have exaggerated and discontinuous features.
The wavelet itself is a discontinuous and jagged curve when rendered, and by combining several wavelets, a much improved representation of image features is available.
Another transform known as the Discrete Wavelet Transform (DWT), based on Wavelet analysis, has been developed to better represent discontinuities such as in the edge features of digital images.
A fundamental difference between Fourier transforms and Wavelet transforms is that Wavelet transforms are localized in space as well as time.
Thus, both space (time-domain) and frequency are preserved when a signal is decomposed by Wavelet analysis.
Since the Fourier transform is periodic in nature, it does not well represent spatial discontinuities, whereas as the Wavelet transform is by nature discontinuous and has localized variations that disappear or do not exist in all locations of the signal.
General Wavelet theory is quite known in the art of signal analysis and will not be described so as not to obscure the invention
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Image Analysis 
