Depth-of-field simulation via optical-based depth extraction

1. A method of depth-of-field simulation, comprising:

receiving a first image;

receiving a second image;

predicting a layer of interest mask of the first image;

determining a first mean brightness anchor value of a first window array of the layer of interest mask;

determining a second mean brightness anchor value of a second window array of the layer of interest mask;

setting a first set of binary codes of the first window array;

setting a second set of binary codes of the second window array;

determining a hamming distance between the first set of binary codes and the second set of binary codes;

determining a cost volume based on the hamming distance;

resampling a vertical cost based on a vertical ordinal direction the cost volume;

resampling a horizontal cost based on a horizontal ordinal direction the cost volume;

determining an all-in-focus layer based on the vertical cost and the horizontal cost;

determining an out-of-focus layer based on the vertical cost and the horizontal cost; and

determining a depth of the all-in-focus layer and out-of-focus layer.

2. The method of claim 1 , further comprising semantic partitioning of the cost volume.

3. The method of claim 1 , further comprising edge a filtering the all-in-focus layer.

4. The method of claim 3 , further comprising smooth filtering the out-of-focus layer.

5. The method of claim 4 , further comprising recombining the edge filtered ail-in-focus layer and the smooth filtered out-of-focus layer.

6. The method of claim 1 , further comprising decoupling a spatial domain and a depth domain of the first image and the second image.

7. The method of claim 6 , further comprising estimating an all-in-focus depth of the all-in-focus layer.

8. The method of claim 7 , further comprising segregating the all-in-focus layer and the out-of-focus layer.

9. A method of depth-of-field simulation, comprising:

receiving a plurality of images;

predicting a layer of interest mask of the plurality of images;

determining a plurality of mean brightness anchor values of a respective plurality of layers of interest window arrays of the layer of interest mask within a respective plurality of images;

setting a plurality of layers of interest set of binary codes of the respective plurality of layers of interest window arrays;

determining a hamming distance between plurality of layers of interest set of binary codes of the respective plurality of layers of interest window arrays;

determining a cost volume based on the hamming distance;

resampling a vertical cost based on a vertical ordinal direction the cost volume;

resampling a horizontal cost based on a horizontal ordinal direction the cost volume;

determining an all-in-focus layer based on the vertical cost and the horizontal cost;

determining an out-of-focus layer based on the vertical cost and the horizontal cost; and

determining a depth of the all-in-focus layer and out-of-focus layer.

10. The method of claim 9 , further comprising semantic partitioning of the cost volume.

11. The method of claim 9 , further comprising edge filtering the all-in-focus layer.

12. The method of claim 11 , further comprising smooth filtering the out-of-focus layer.

13. The method of claim 12 , further comprising recombining the edge filtered all-in-focus layer and the smooth filtered out-of-focus layer.

14. The method of claim 9 , further comprising decoupling a spatial domain and a depth domain of the plurality of images.

15. The method of claim 14 , further comprising estimating an all-in-focus depth of the all-in-focus layer.

16. The method of claim 15 , further comprising segregating the all-in-focus layer and the out-of-focus layer.