Imaging system with depth estimation mechanism and method of operation thereof

1. A method of operation of an imaging system comprising:

receiving an input sequence with an image pair, the image pair having a first image and a second image;

determining a first patch from the first image and a second patch from the second image;

generating an iteration map from the first patch of the first image and the second patch of the second image;

generating a final depth map from the iteration map, wherein generating the final depth map includes:

generating a variance depth map including a maximum variance; and

generating a merged map by combining the variance depth map and the iteration map; and

determining a focus position from the final depth map.

2. The method as claimed in claim 1 wherein generating the iteration map includes:

determining an iteration number between the first patch in the first image and the second patch in the second image; and

detecting a zero crossing point from the iteration number.

3. The method as claimed in claim 1 wherein generating the final depth map includes extracting a plurality of lens position based on a histogram of the iteration map.

4. The method as claimed in claim 1 wherein receiving the input sequence includes receiving a plurality of defocused images.

5. A method of operation of an imaging system comprising:

receiving an input sequence with a plurality of defocused images, the plurality of defocused images includes a first image and a second image;

determining a first patch for the first image and a second patch for the second image;

generating an iteration map from the first patch of the first image and from the second patch of the second image;

generating a final depth map from the iteration map, wherein generating the final depth map includes:

generating a histogram from the iteration map, the histogram includes a histogram width; and

determining a number of required images from the histogram width; and determining a focus position from the final depth map.

6. The method as claimed in claim 5 further comprising generating a confidence map based on the iteration map.

7. The method as claimed in claim 5 wherein determining the focus position includes:

generating a variance depth map from the input sequence, the variance depth map includes a maximum variance;

combining the variance depth map with the iteration map; and

identifying a zero crossing at the maximum variance as the focus position.

8. The method as claimed in claim 5 further comprising generating a high frequency periodic pattern mask based on the iteration map.

9. An imaging system comprising:

a non-transitory memory configured for storing:

a capture module for receiving an input sequence with an image pair, the image pair having a first image and a second image and for determining a first patch from the first image and a second patch from the second image, wherein the capture module is for receiving the input sequence with a plurality of defocused images including the first image, the second image, and a third image;

an iteration module for generating an iteration map from the first patch of the first image and the second patch of the second image; and

a position module for generating a final depth map from the iteration map and for determining a focus position from the final depth maps;

a device control unit for generating a high frequency periodic pattern mask based on the iteration map; and

a processor configured for processing the capture module, the iteration module and the position module.

10. The system as claimed in claim 9 wherein:

the position module is for generating a variance depth map including a maximum variance; and

further compromising:

a device control unit for generating a merged map by combining the variance depth map and the iteration map.

11. The system as claimed in claim 9 wherein:

the iteration module is for determining an iteration number between the first patch in the first image and the second patch in the second image; and

further comprising:

a device control unit for detecting a zero crossing from the iteration number.

12. The system as claimed in claim 9 wherein the position module is for extracting a plurality of a lens position based on a histogram of the iteration map.

13. The system as claimed in claim 9 wherein the capture module is for receiving a plurality of defocused images.

14. The system as claimed in claim 9 further comprising a confidence map based on the iteration map.

15. The system as claimed in claim 9 further comprising a histogram module for generating a histogram from the iteration map, the histogram includes a histogram width and for determining a number of required images from the histogram width.

16. The system as claimed in claim 9 further comprising a device control unit for generating a variance depth map from the input sequence, the variance depth map includes a maximum variance, for combining the variance depth map with the iteration map, and for identifying a zero crossing at the maximum variance as the focus position.