Systems and methods for generating depth maps using images captured by camera arrays

• US 9,060,120 B2
• Filed: 07/30/2014
• Issued: 06/16/2015
• Est. Priority Date: 05/20/2008
• Status: Active Grant

• Current Assignee: FotoNation Limited (Adeia Inc.)
• Original Assignee: Pelican Imaging Corporation
• Inventors: Venkataraman, Kartik, Jabbi, Amandeep S., Mullis, Robert H.
• Primary Examiner(s): LAM, HUNG H
• Application Number:
  US14/447,508
• Publication Number:
  US 20140333764A1
• Time in Patent Office: 321 Days
• Field of Search: 348/208.1, 348/221.2, 348/2, 348/229.1, 348/234, 348/362
• US Class Current: 1/1
• CPC Class Codes: G02B 13/0015 characterised by the lens design
• CPC Class Codes: G02B 13/0085 employing wafer level optics
• CPC Class Codes: G02B 27/0025 for optical correction, e.g. distorsion, aberration
• CPC Class Codes: G02B 27/1066 for enhancing image performance, like resolution, pixel numbers, dual magnifications or dynamic range, by tiling, slicing or overlapping fields of view
• CPC Class Codes: G02B 27/123 The splitting element being a lens or a system of lenses, including arrays and surfaces with refractive power
• CPC Class Codes: G02B 3/0006 Arrays G02B3/02, G02B5/188 take precedence
• CPC Class Codes: G02B 5/20 Filters polarising elements G02B5/30
• CPC Class Codes: G02B 5/208 for use with infrared or ultraviolet radiation, e.g. for separating visible light from infrared and/or ultraviolet radiation
• CPC Class Codes: G06T 1/20 Processor architectures; Processor configuration, e.g. pipelining
• CPC Class Codes: G06T 2207/10052 Images from lightfield camera
• CPC Class Codes: G06T 2207/30244 Camera pose
• CPC Class Codes: G06T 3/4053 based on super-resolution, i.e. the output image resolution being higher than the sensor resolution
• CPC Class Codes: G06T 7/50 Depth or shape recovery
• CPC Class Codes: G06T 7/55 from multiple images
• CPC Class Codes: G06T 7/557 from light fields, e.g. from plenoptic cameras
• CPC Class Codes: G06T 7/70 Determining position or orientation of objects or cameras camera calibration G06T7/80
• CPC Class Codes: H01L 27/14618 Containers
• CPC Class Codes: H01L 27/14621 Colour filter arrangements
• CPC Class Codes: H01L 27/14625 Optical elements or arrangements associated with the device
• CPC Class Codes: H01L 27/14632 Wafer-level processed structures
• CPC Class Codes: H01L 27/14685 : Process for coatings or optical elements
• CPC Class Codes: H01L 27/14687 : Wafer level processing
• CPC Class Codes: H01L 2924/00 : Indexing scheme for arrangements or methods for connecting or disconnecting semiconductor or solid-state bodies as covered by H01L24/00
• CPC Class Codes: H01L 2924/0002 : Not covered by any one of groups H01L24/00, H01L24/00 and H01L2224/00
• CPC Class Codes: H04N 1/195 : the array comprising a two-dimensional array or a combination of two-dimensional arrays
• CPC Class Codes: H04N 1/19589 : Optical means, e.g. an optical fibre bundle, for mapping the whole or a part of a scanned image onto the array
• CPC Class Codes: H04N 1/19594 : using a television camera or a still video camera
• CPC Class Codes: H04N 13/128 : Adjusting depth or disparity
• CPC Class Codes: H04N 13/243 : using three or more 2D image sensors
• CPC Class Codes: H04N 13/257 : Colour aspects
• CPC Class Codes: H04N 2013/0081 : Depth or disparity estimation from stereoscopic image signals
• CPC Class Codes: H04N 23/10 : for generating image signals from different wavelengths
• CPC Class Codes: H04N 23/11 : for generating image signals from visible and infrared light wavelengths
• CPC Class Codes: H04N 23/20 : for generating image signals from infrared radiation only
• CPC Class Codes: H04N 23/40 : Circuit details for pick-up tubes
• CPC Class Codes: H04N 23/45 : for generating image signals from two or more image sensors being of different type or operating in different modes, e.g. with a CMOS sensor for moving images in combination with a charge-coupled device [CCD] for still images
• CPC Class Codes: H04N 23/54 : Mounting of pick-up tubes, electronic image sensors, deviation or focusing coils
• CPC Class Codes: H04N 23/60 : Control of cameras or camera modules
• CPC Class Codes: H04N 23/69 : Control of means for changing angle of the field of view, e.g. optical zoom objectives or electronic zooming
• CPC Class Codes: H04N 23/698 : for achieving an enlarged field of view, e.g. panoramic image capture
• CPC Class Codes: H04N 23/80 : Camera processing pipelines; Components thereof
• CPC Class Codes: H04N 23/90 : Arrangement of cameras or camera modules, e.g. multiple cameras in TV studios or sports stadiums
• CPC Class Codes: H04N 23/951 : by using two or more images to influence resolution, frame rate or aspect ratio
• CPC Class Codes: H04N 23/958 : for extended depth of field imaging
• CPC Class Codes: H04N 25/00 : Circuitry of solid-state image sensors [SSIS]; Control thereof
• CPC Class Codes: H04N 25/131 : including elements passing infrared wavelengths
• CPC Class Codes: H04N 25/134 : based on three different wavelength filter elements
• CPC Class Codes: H04N 25/135 : based on four or more different wavelength filter elements
• CPC Class Codes: H04N 25/41 : Extracting pixel data from a plurality of image sensors simultaneously picking up an image, e.g. for increasing the field of view by combining the outputs of a plurality of sensors
• CPC Class Codes: H04N 25/534 : depending on the spectral component
• CPC Class Codes: H04N 25/581 : acquired simultaneously
• CPC Class Codes: H04N 25/60 : Noise processing, e.g. detecting, correcting, reducing or removing noise
• CPC Class Codes: H04N 25/616 : involving a correlated sampling function, e.g. correlated double sampling [CDS] or triple sampling
• CPC Class Codes: H04N 25/63 : applied to dark current
• CPC Class Codes: H04N 25/68 : applied to defects
• CPC Class Codes: H04N 25/74 : Circuitry for scanning or addressing the pixel array
• CPC Class Codes: H04N 5/262 : Studio circuits, e.g. for mixing, switching-over, change of character of image, other special effects ; Cameras specially adapted for the electronic generation of special effects
• CPC Class Codes: H04N 5/2621 : Cameras specially adapted for the electronic generation of special effects during image pickup, e.g. digital cameras, camcorders, video cameras having integrated special effects capability
• CPC Class Codes: H04N 5/265 : Mixing
• CPC Class Codes: H04N 5/33 : Transforming infrared radiation cameras or camera modules for generating image signals from infrared radiation H04N23/20; circuitry of SSIS for transforming infrared radiation into image signals H04N25/20

Abstract

A camera array, an imaging device and/or a method for capturing image that employ a plurality of imagers fabricated on a substrate is provided. Each imager includes a plurality of pixels. The plurality of imagers include a first imager having a first imaging characteristics and a second imager having a second imaging characteristics. The images generated by the plurality of imagers are processed to obtain an enhanced image compared to images captured by the imagers. Each imager may be associated with an optical element fabricated using a wafer level optics (WLO) technology.

Claims

1. A method of generating a depth map from a plurality of images captured from different viewpoints, comprising:

normalizing a set of images using calibration data stored in a storage device using an address conversion module, where;

the set of images comprises a plurality of images that are;

captured from different viewpoints;

include different occlusions sets;

wherein the occlusion set of a first image is the portion of a scene visible in a second image that is occluded in the first image; and

the set of images includes;

images captured using the same imaging characteristics; and

images captured using different imaging characteristics;

detecting and metering parallax using a parallax confirmation and measurement module, where detecting and metering parallax comprises;

determining the parallax that yields the highest correlation between pixels from images captured using the same imaging characteristics accounting for the positions of the imagers that captured the images by;

performing redundant pair-wise measurements between pixels from images captured using the same imaging characteristics to determine pixel correlation for different parallax-induced shifts;

keeping track of various pair-wise measurements and calculating a parallax difference that yields the highest pixel correlation ignoring pixels in the images that are in an exposed occlusion set; and

generating a depth map using the parallax confirmation and measurement module based upon the measured parallax.

2. The method of claim 1, wherein:

the calibration data indicates mappings between addresses of physical pixels in imagers and logical addresses within an image; and

the address conversion module resolves phase differences between the set of images by converting addresses of physical pixels in imagers to logical addresses.

3. The method of claim 1, wherein calculating a parallax difference that yields the highest pixel correlation comprises determining parallax based upon the set of pair-wise measurements that have the best least squares fit.

4. The method of claim 1, wherein aligning portions of images captured by different imagers to compensate for parallax using an image pixel correlation module further comprises determining appropriate X and Y offsets to be applied to logical pixel address calculations using an address conversion module based upon the detected and metered parallax and the stored calibration data.

5. The method of claim 1, further comprising:

aligning portions of different images to compensate for parallax using an image pixel correlation module based upon the measured parallax and the stored calibration data; and

obtaining a higher resolution image having a resolution that is higher than the resolutions of the images in the set of images using a super-resolution module.

6. The method of claim 5, wherein obtaining a higher resolution image having a resolution that is higher than the resolutions of the images in the set of images comprises fusing the aligned image portions.

7. The method of claim 6, wherein obtaining a higher resolution image having a resolution that is higher than the resolutions of the images comprises performing super-resolution processing on the fused image portions to synthesize a super-resolution image.

8. The method of claim 5, wherein obtaining a higher resolution image having a resolution that is higher than the resolutions of the images in the set of images comprises performing super-resolution processing on the aligned image portions to synthesize a super-resolution image.

9. The method of claim 5, further comprising selecting at least one distance as a focal plane and applying blurring to pixels in the higher resolution image with depths in the depth map that are not proximate a focal plane using the super-resolution processing module.

10. The method of claim 1, further comprising estimating distance to an object visible in the set of images using the parallax confirmation and measurement module.

11. The method of claim 1, further comprising measuring the absolute size and shape of a physical item visible in the set of images using the parallax confirmation and measurement module.

12. The method of claim 1, wherein the set of images is captured by an array of between 2×

2 and 6×

6 imagers.

13. The method of claim 1, wherein the set of images is captured by a set of imagers that form at least a 3×

3 configuration of imagers comprising;

an imager having a green filter at the center of a 3×

3 configuration of imagers;

two imagers having red filters located on opposite sides of the 3×

3 configuration of imagers;

two imagers having blue filters located on opposite sides of the 3×

3 configuration of imagers; and

four imagers having green filters surrounding the central imager having a green filter.

14. The method of claim 1, wherein the set of images is captured by a linear array of imagers.

15. The method of claim 1, wherein the linear array of imagers is selected from the group consisting of:

a 1×

4 array of imagers; and

a 1×

10 array of imagers.

16. The method of claim 1, wherein each image in the set of images is obtained by an imager including a filter selected from the group consisting of a Bayer filter, one or more Blue filters, one or more Green filters, one or more Red filters, one or more shifted spectral filters, one or more near-IR filters, and one or more hyper-spectral filters.

17. The method of claim 1, wherein at least one of the images in the set of images is captured by an imager selected from the group consisting of:

a polychromatic imager; and

a near-IR imager.

18. The method of claim 1, wherein imagers in the array of imagers have the same resolution.

19. The method of claim 1, wherein the images in the set of images have different resolutions.

20. A method of generating a depth map from a plurality of images captured from different viewpoints, comprising:

normalizing a set of images using calibration data stored in a storage device using an address conversion module, where;

the set of images comprises a plurality of images that are;

captured from different viewpoints;

include different occlusions sets;

wherein the occlusion set of a first image is the portion of a scene visible in a second image that is occluded in the first image; and

the set of images includes;

images captured using the same imaging characteristics; and

images captured using different imaging characteristics;

detecting and metering parallax using a parallax confirmation and measurement module, where detecting and metering parallax comprises;

determining the parallax that yields the highest correlation between pixels from images captured using the same imaging characteristics accounting for the positions of the imagers that captured the images by;

performing redundant pair-wise measurements between pixels from images captured using the same imaging characteristics to determine pixel correlation for different parallax-induced shifts;

keeping track of various pair-wise measurements and calculating a parallax difference that yields the highest pixel correlation ignoring pixels in the images that are in an exposed occlusion set; and

aligning portions of different images to compensate for parallax using an image pixel correlation module based upon the measured parallax and the stored calibration data; and

obtaining a higher resolution image having a resolution that is higher than the resolutions of the images in the set of images using a super-resolution module; and

generating a depth map for the higher resolution image using the parallax confirmation and measurement module based upon the measured parallax;

wherein obtaining an image having a resolution that is higher than the resolutions of the images in the set of images comprises;

fusing the aligned image portions;

performing super-resolution processing on the fused image portions to synthesize a super-resolution image; and

selecting at least one distance as a focal plane and applying blurring to pixels in the higher resolution image with depths in the depth map that are not proximate a focal plane.