# US 2011/0032413 A1 · Auto-focusing techniques based on statistical blur estimation and associated systems and methods

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- Number: US 2011/0032413 A1
- Document ID: US20110032413A1
- Status: Published application
- Kind: A1
- Application number: 12/535,331
- Filed: 2009-08-04
- Published: 2011-02-10
- Earliest claimed: 2009-08-04
- Assignees: Aptina Imaging Corporation
- Inventors: Prajit Kulkarni
- Web page: https://patentgrep.com/patent/US20110032413A1

## CPC

- G03B 13/36: Autofocus systems
- G03B: APPARATUS OR ARRANGEMENTS FOR TAKING PHOTOGRAPHS OR FOR PROJECTING OR VIEWING THEM; APPARATUS OR ARRANGEMENTS EMPLOYING ANALOGOUS TECHNIQUES USING WAVES OTHER THAN OPTICAL WAVES; ACCESSORIES THEREFOR (optical parts of such apparatus G02B; photosensitive materials or processes for photographic purposes G03C; apparatus for processing exposed photographic materials G03D)

## Abstract

This is generally directed to auto-focusing techniques based on statistical blur estimation. An image can be captured at two or more candidate lens positions. The amount of blur of each image can then be determined, and the image containing the least amount of blur can be chosen as the “in-focus” image. In some embodiments, the amount of blur of an image can be determined by identifying how “Gaussian” an image is. Characteristics that are more Gaussian in nature can indicate that the image is more blurry. The Gaussianity of an image can be determined by estimating a generalized Gaussian shape parameter for that image. A smaller shape parameter can indicate the image is less Gaussian in nature. The shape parameter can be estimated in any suitable manner such as, for example, through a 2-d discrete wavelet transform, through a 1-d discrete wavelet transform, or through any other suitable manner.

## Claims

### Claim 1

A method of auto-focusing an imaging system, the method comprising:
capturing a plurality of images, wherein each image is captured at a different candidate lens position of the imaging system;
determining a Gaussianity value of each image of the plurality of images;
identifying a particular image of the plurality of images that comprises a smallest Gaussianity value; and
moving a lens of the imaging system to a particular candidate lens position, wherein the particular image was captured at the particular candidate lens position.

### Claim 2 (depends on 1)

The method of claim 1, wherein determining a Gaussianity value comprises:
applying a first stage of a discrete wavelet transform (“DWT”) to each image of the plurality of images, wherein the application of the first stage of the DWT produces at least one high frequency subband for each image; and
determining, for each image, a Gaussianity value of the at least one high frequency subband for each image.

### Claim 3 (depends on 2)

The method of claim 2, wherein the DWT comprises one of a Cohen-Daubechies-Feauveau 9/7 wavelet kernel and a spline 5/3 wavelet kernel.

### Claim 4 (depends on 2)

The method of claim 2, wherein applying a first stage of a DWT to each image of the plurality of image comprises:
applying the first stage of the DWT to a Region of Interest of each image of the plurality of images.

### Claim 5 (depends on 1)

The method of claim 1, wherein determining a Gaussianity value comprises:
determining a shape parameter value for each image of the plurality of images.

### Claim 6 (depends on 5)

The method of claim 5, wherein identifying a particular image of the plurality of images comprises:
identifying a particular image of the plurality of images that comprises a shape parameter value farthest from a value of two.

### Claim 7 (depends on 5)

The method of claim 5, wherein identifying a particular image of the plurality of images comprises:
identifying a particular image of the plurality of images that comprises a shape parameter value that is least in value.

### Claim 8 (depends on 5)

The method of claim 5, wherein determining a shape parameter value comprises:
estimating the shape parameter value by an nth order Method of Moments.

### Claim 9

A method comprising:
capturing a plurality of images of a scene, wherein each image is captured at a different candidate lens position of an imaging system;
performing a two-dimensional discrete wavelet transform (“2-d DWT”) on each image of the plurality of images to produce at plurality of subbands for each image, wherein the plurality of subbands comprises at least a HL1 subband, a LH1 subband, and a HH1 subband;
determining, for each image, a shape parameter value for each of the HL1 subband, the LH1 subband, and the HH1 subband;
determining, for each image, a final shape parameter value based on the shape parameter values of the HL1 subband, the LH1 subband, and the HH1 subband; and
identifying a particular image of the plurality of images that comprises a smallest final shape parameter value.

### Claim 10 (depends on 9)

The method of claim 9, wherein determining, for each image, a final shape parameter value comprises:
adding the shape parameter value of the HL1 subband, the shape parameter value of the LH1 subband, and the shape parameter value of the HH1 subband.

### Claim 11 (depends on 9)

The method of claim 9, wherein determining, for each image, a final shape parameter value comprises:
averaging the shape parameter values of the HL1 subband, the LH1 subband, and the HH1 subband.

### Claim 12 (depends on 9)

The method of claim 9, wherein determining, for each image, a shape parameter value for each of the HL1 subband, the LH1 subband, and the HH1 subband comprises:
generating a lookup table comprising a plurality of Ratio Function values and a plurality of corresponding shape parameter values;
determining, for each image, a Ratio Function value for each of the HL1 subband, the LH1 subband, and the HH1 subband; and
determining, for each image, the shape parameter values of the HL1 subband, the LH1 subband, and the HH1 subband based on the generated lookup table and the determined Ratio Function values.

### Claim 13 (depends on 9)

The method of claim 9, further comprising:
moving a lens of the imaging system to a particular candidate lens position, wherein the particular image was captured at the particular candidate lens position.

### Claim 14

An auto-focusing system, the system comprising:
a lens operable to capture an image, wherein the image comprises a plurality of pixels grouped into a plurality of pixel sets;
a lens driver operable to move the lens to a plurality of lens positions, wherein the image is captured at a certain lens position of the plurality of lens positions; and
a processor operable to:
sequentially process each pixel set by applying, on each luma pixel location of the plurality of pixel sets, a one-dimensional discrete wavelet transform (“1-d DWT”) to produce a luma component coefficient for each luma pixel location;
determine a shape parameter value for each pixel set based on each pixel set's generated luma component coefficients; and
determine a final shape parameter value for the image based on the shape parameter values of each pixel set; and wherein:
the auto-focusing system is operable to similarly generate a final shape parameter value for each of a plurality of images, wherein each image of the plurality of images is captured at a different lens position.

### Claim 15 (depends on 14)

The system of claim 14, wherein the processor is further operable to:
identify a particular image of the plurality of images that is associated with a smallest final shape parameter value; and
direct the lens driver to move the lens to a particular lens position at which the particular image was captured.

### Claim 16 (depends on 14)

The system of claim 14, wherein the processor is further operable to:
sequentially process each pixel set by performing, on each green pixel location of a raw Bayer array of the image, a 1-d DWT on each diagonal to produce a first diagonal coefficient and a second diagonal coefficient for each green pixel location; and
determine a shape parameter value for each pixel set based on each pixel set's luma component coefficients, first diagonal coefficients, and second diagonal coefficients.

### Claim 17 (depends on 14)

The system of claim 14, wherein the processor is further operable to:
update, at each luma pixel location of a pixel set, a sum of luma component coefficients, a sum of luma component coefficients squared, a sum of an absolute value of luma component coefficients; and
determine, at the end of each pixel set, a variance and a first absolute moment for each pixel set based on that pixel set's updated sum of luma component coefficients, updated sum of luma component coefficients squared, and updated sum of the absolute value of luma component coefficients.

### Claim 18 (depends on 17)

The system of claim 17, wherein the processor is further operable to:
determine a Ratio Function value for each pixel set based on each pixel set's variance and first absolute moment; and
determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value.

### Claim 19 (depends on 18)

The system of claim 18, further comprising:
a memory operable to store a lookup table, wherein the lookup table comprises a plurality of Ratio Function values and a plurality of corresponding shape parameter values, and wherein:
the processor is further operable to determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value and the lookup table.

### Claim 20 (depends on 19)

The system of claim 19, wherein the plurality of Ratio Function values are scaled by a first scaling factor and the plurality of shape parameter values are scaled by a second scaling factor, and wherein the first and second scaling factor convert the plurality Ratio Function values and the plurality of shape parameter values into integer form.

### Claim 21 (depends on 20)

The system of claim 20, wherein the first and second scaling factors are each a power of two.

### Claim 22 (depends on 18)

The system of claim 18, further comprising:
a memory operable to store a lookup table, wherein the lookup table comprises a plurality of knee points of a Ratio Function curve, and wherein:
the processor is further operable to determine, for each pixel set, a shape parameter value by:
identifying at least two knee points of the plurality of knee points based on the Ratio Function value of that pixel set; and
linearly interpolating the shape parameter value from that pixel set's determined at least two knee points.

### Claim 23 (depends on 22)

The system of claim 22, wherein the knee points are determined by calculating a first derivative of the Ratio Function curve.

### Claim 24 (depends on 18)

The system of claim 18, further comprising:
a memory operable to store coefficients of a parametric function, wherein the parametric function approximates a Ratio Function curve; and wherein:
the processor is operable to determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value and based on the coefficients of the parametric function.

### Claim 25

An auto-focusing system, the system comprising:
a lens operable to capture an image, wherein the image comprises a plurality of pixels grouped into a plurality of pixel sets;
a lens driver operable to move the lens to a plurality of lens positions, wherein the image is captured at a certain lens position of the plurality of lens positions;
a high-pass filter operable to sequentially process each pixel set by filtering each luma pixel location of the plurality of pixel sets, wherein the filtering generates a luma component coefficient for each luma pixel location; and
a processor operable to:
determine a shape parameter value for each pixel set based on each pixel set's generated luma component coefficients; and
determine a final shape parameter value for the image based on the shape parameter values of each pixel set; and wherein:
the auto-focusing system is operable to similarly generate a final shape parameter value for each of a plurality of images, wherein each image of the plurality of images is captured at a different lens position.

### Claim 26 (depends on 25)

The system of claim 25, wherein the processor is further operable to:
identify a particular image of the plurality of images that is associated with a final shape parameter value farthest from a value of two; and
direct the lens driver to move the lens to a particular lens position at which the particular image was captured.

### Claim 27 (depends on 25)

The system of claim 25, wherein the high-pass filter comprises at least three taps.

### Claim 28 (depends on 25)

The system of claim 25, wherein the processor is further operable to:
determine a variance and first absolute moment for each pixel set;
determine a Ratio Function value for each pixel set based on each pixel set's variance and first absolute moment; and
determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value.

### Claim 29 (depends on 28)

The system of claim 28, further comprising:
a memory operable to store a lookup table, wherein the lookup table comprises a plurality of Ratio Function values and a plurality of corresponding shape parameter values, and wherein:
the processor is further operable to determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value and the lookup table.

### Claim 30 (depends on 29)

The system of claim 29, wherein the plurality of Ratio Function values are scaled by a first scaling factor and the plurality of shape parameter values are scaled by a second scaling factor, and wherein the first and second scaling factor convert the plurality Ratio Function values and the plurality of shape parameter values into integer form.

### Claim 31 (depends on 30)

The system of claim 30, wherein the first and second scaling factors are each a power of two.

### Claim 32 (depends on 28)

The system of claim 28, further comprising:
a memory operable to store a lookup table, wherein the lookup table comprises a plurality of knee points of a Ratio Function curve, and wherein:
the processor is further operable to determine, for each pixel set, a shape parameter value by:
identifying at least two knee points of the plurality of knee points based on the Ratio Function value of that pixel set; and
linearly interpolating the shape parameter value from that pixel set's determined at least two knee points.

### Claim 33 (depends on 32)

The system of claim 32, wherein the knee points are determined by calculating a first derivative of the Ratio Function curve.

### Claim 34 (depends on 28)

The system of claim 28, further comprising:
a memory operable to store coefficients of a parametric function, wherein the parametric function approximates a Ratio Function curve; and wherein:
the processor is operable to determine a shape parameter value for each pixel set based on each pixel set's Ratio Function value and based on the coefficients of the parametric function.