초록 ▼

An autofocus method includes acquiring multiple images each having a camera lens focused at a different focus distance. A sharpest image is determined among the multiple images. Horizontal, vertical and/or diagonal integral projection (IP) vectors are computed for each of the multiple images. One or

An autofocus method includes acquiring multiple images each having a camera lens focused at a different focus distance. A sharpest image is determined among the multiple images. Horizontal, vertical and/or diagonal integral projection (IP) vectors are computed for each of the multiple images. One or more IP vectors of the sharpest image is/are convoluted with multiple filters of different lengths to generate one or more filtered IP vectors for the sharpest image. Differences are computed between the one or more filtered IP vectors of the sharpest image and one or more IP vectors of at least one of the other images of the multiple images. At least one blur width is estimated between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range. The steps are repeated one or more times to obtain a sequence of estimated blur width values. A focus position is adjusted based on the sequence of estimated blur width values.

대표청구항 ▼

1. A method of automatically focusing an image acquisition device on a scene, comprising the steps of: (a) acquiring multiple images each having a focusing means of the an image acquisition device focused at a different focus distance;(b) determining a sharpest image among the multiple images;(c) co

1. A method of automatically focusing an image acquisition device on a scene, comprising the steps of: (a) acquiring multiple images each having a focusing means of the an image acquisition device focused at a different focus distance;(b) determining a sharpest image among the multiple images;(c) computing horizontal, vertical or diagonal integral projection vectors, or combinations thereof, for each of the multiple images;(d) convoluting one or more integral projection vectors of the sharpest image with multiple filters of different lengths to generate one or more filtered integral projection vectors for the sharpest image;(e) computing differences between the one or more filtered integral projection vectors of the sharpest image and one or more integral projection vectors of at least one of the other images of the multiple images;(f) estimating at least one blur width between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range;(g) repeating steps (b)-(f) one or more times to obtain a sequence of estimated blur width values; and(h) adjusting a focus position based on the sequence of estimated blur width values. 2. The method of claim 1, wherein the determining said sharpest image comprises a gradient image-based process. 3. The method of claim 1, wherein the differences comprise absolute sums of differences. 4. The method of claim 1, wherein the differences comprise sums of squared differences. 5. The method of claim 1, wherein the multiple filters of different lengths comprise integral projection vectors of blur kernels of different widths. 6. The method of claim 5, wherein the blur kernels comprise Gaussian or circular averaging kernels, or combinations thereof. 7. The method of claim 1, further comprising downscaling or cropping lengths of the integral projection vectors in order to reduce complexity. 8. The method of claim 1, further comprising applying a descent process to reduce a number of computations. 9. The method of claim 1, further comprising computing errors at different interval lengths in order to avoid one or more local minima. 10. The method of claim 1, wherein the adjusting of the focus position comprises a smaller or greater adjustment, respectively, for smaller or greater estimated blur width values. 11. The method of claim 1, further comprising: estimating a focus kernel difference between the sharpest image and the at least one of the other images of the multiple images; andcomputing an approximate focus level position based on the estimated focus kernel difference. 12. The method of claim 11, further comprising refining the focus level position by acquiring one or more images close to the approximate focus level position and applying a gradient method to determine a best match. 13. A image acquisition device that automatically focuses on a scene, comprising: (a) a focusing means and image capture component for acquiring multiple images each having the focusing means focused at a different focus distance;(b) a processorprogrammed to perform an autofocus method, including the steps of: (i) determining a sharpest image among the multiple images;(ii) computing horizontal, vertical or diagonal integral projection vectors, or combinations thereof, for each of the multiple images;(iii) convoluting one or more integral projection vectors of the sharpest image with multiple filters of different lengths to generate one or more filtered integral projection vectors for the sharpest image;(iv) computing differences between the one or more filtered integral projection vectors of the sharpest image and one or more integral projection vectors of at least one of the other images of the multiple images;(v) estimating at least one blur width between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range;(vi) repeating steps (i)-(v) one or more times to obtain a sequence of estimated blur width values; and(vii) adjusting a focus position based on the sequence of estimated blur width values. 14. The device of claim 13, wherein the determining said sharpest image comprises a gradient image-based process. 15. The device of claim 13, wherein the differences comprise absolute sums of differences. 16. The device of claim 13, wherein the differences comprise sums of squared differences. 17. The device of claim 13, wherein the multiple filters of different lengths comprise integral projection vectors of blur kernels of different widths. 18. The device of claim 17, wherein the blur kernels comprise Gaussian or circular averaging kernels, or combinations thereof. 19. The device of claim 13, wherein the method further comprises downscaling or cropping lengths of the integral projection vectors in order to reduce complexity. 20. The device of claim 13, wherein the method further comprises applying a descent process to reduce a number of computations. 21. The device of claim 13, wherein the method further comprises computing errors at different interval lengths in order to avoid one or more local minima. 22. The device of claim 13, wherein the adjusting of the focus position comprises a smaller or greater adjustment, respectively, for smaller or greater estimated blur width values. 23. The device of claim 13, wherein the method further comprises: estimating a focus kernel difference between the sharpest image and the at least one of the other images of the multiple images; andcomputing an approximate focus level position based on the estimated focus kernel difference. 24. The device of claim 23, wherein the method further comprises refining the focus level position by acquiring one or more images close to the approximate focus level position and applying a gradient method to determine a best match. 25. One or more non-transitory, processor-readable devices having code embedded therein for programming a processor to perform a method of automatically focusing an image acquisition device on a scene, the method comprising the steps of: (a) acquiring multiple images each having a focusing means of an image acquisition device focused at a different focus distance;(b) determining a sharpest image among the multiple images;(c) computing horizontal, vertical or diagonal integral projection vectors, or combinations thereof, for each of the multiple images;(d) convoluting one or more integral projection vectors of the sharpest image with multiple filters of different lengths to generate one or more filtered integral projection vectors for the sharpest image;(e) computing differences between the one or more filtered integral projection vectors of the sharpest image and one or more integral projection vectors of at least one of the other images of the multiple images;(f) estimating at least one blur width between the sharpest image and the at least one of the other images of the multiple images as a minimum value among the computed differences over a selected range;(g) repeating steps (b)-(f) one or more times to obtain a sequence of estimated blur width values; and(h) adjusting a focus position based on the sequence of estimated blur width values. 26. The one or more non-transitory, processor-readable media of claim 25, wherein the determining said sharpest image comprises a gradient image-based process. 27. The one or more non-transitory, processor-readable media of claim 25, wherein the differences comprise absolute sums of differences. 28. The one or more non-transitory, processor-readable media of claim 25, wherein the differences comprise sums of squared differences. 29. The one or more non-transitory, processor-readable media of claim 25, wherein the multiple filters of different lengths comprise integral projection vectors of blur kernels of different widths. 30. The one or more non-transitory, processor-readable media of claim 29, wherein the blur kernels comprise Gaussian or circular averaging kernels, or combinations thereof. 31. The one or more non-transitory, processor-readable media of claim 25, wherein the method further comprises downscaling or cropping lengths of the integral projection vectors in order to reduce complexity. 32. The one or more non-transitory, processor-readable media of claim 25, wherein the method further comprises applying a descent process to reduce a number of computations. 33. The one or more non-transitory, processor-readable media of claim 25, wherein the method further comprises computing errors at different interval lengths in order to avoid one or more local minima. 34. The one or more non-transitory, processor-readable media of claim 25, wherein the adjusting of the focus position comprises a smaller or greater adjustment, respectively, for smaller or greater estimated blur width values. 35. The one or more non-transitory, processor-readable media of claim 25, wherein the method further comprises: estimating a focus kernel difference between the sharpest image and the at least one of the other images of the multiple images; andcomputing an approximate focus level position based on the estimated focus kernel difference. 36. The one or more non-transitory, processor-readable media of claim 35, wherein the method further comprises refining the focus level position by acquiring one or more images close to the approximate focus level position and applying a gradient method to determine a best match.