Systems and Methods for Normalizing Image Data Captured by Camera Arrays

• US 20140354773A1
• Filed: 08/13/2014
• Published: 12/04/2014
• 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., Duparre, Jacques, Hu, Shane Ching-Feng
• Granted Patent: US 9,049,381 B2
• Time in Patent Office: Days
• US Class Current: 348/43
• CPC Class Codes: G02B 13/0015 characterised by the lens design
• CPC Class Codes: G02B 3/0056 arranged along two different directions in a plane, e.g. honeycomb arrangement of lenses G02B3/0043 takes precedence; miniaturised objectives for electronic devices employing wafer level optics G02B13/0085
• CPC Class Codes: G02B 3/0062 Stacked lens arrays, i.e. refractive surfaces arranged in at least two planes, without structurally separate optical elements in-between
• CPC Class Codes: G02B 5/20 Filters polarising elements G02B5/30
• CPC Class Codes: G02B 5/201 in the form of arrays
• CPC Class Codes: G06T 11/60 Editing figures and text; Combining figures or text
• CPC Class Codes: G06T 19/20 Editing of 3D images, e.g. changing shapes or colours, aligning objects or positioning parts
• CPC Class Codes: G06T 2200/04 involving 3D image data
• CPC Class Codes: G06T 2207/10028 Range image; Depth image; 3D point clouds
• CPC Class Codes: G06T 2207/10052 Images from lightfield camera
• CPC Class Codes: G06T 2207/20221 Image fusion; Image merging
• CPC Class Codes: G06T 7/50 Depth or shape recovery
• CPC Class Codes: G06T 7/557 from light fields, e.g. from plenoptic cameras
• CPC Class Codes: H04N 13/128 Adjusting depth or disparity
• CPC Class Codes: H04N 13/239 using two 2D image sensors having a relative position equal to or related to the interocular distance H04N13/243 takes precedence
• 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 23/11 for generating image signals from visible and infrared light wavelengths
• CPC Class Codes: H04N 23/13 with multiple sensors
• CPC Class Codes: H04N 23/16 Optical arrangements associated therewith, e.g. for beam-splitting or for colour correction
• 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 23/55 : Optical parts specially adapted for electronic image sensors; Mounting thereof
• 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 23/88 : for colour balance, e.g. white-balance circuits or colour temperature control
• 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 25/131 : including elements passing infrared wavelengths
• CPC Class Codes: H04N 25/133 : including elements passing panchromatic light, e.g. filters passing white light
• 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 25/48 : Increasing resolution by shifting the sensor relative to the scene
• 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 25/6153 : for colour signals
• CPC Class Codes: H04N 25/67 : applied to fixed-pattern noise, e.g. non-uniformity of response
• CPC Class Codes: H04N 25/705 : Pixels for depth measurement, e.g. RGBZ
• CPC Class Codes: H04N 25/79 : Arrangements of circuitry being divided between different or multiple substrates, chips or circuit boards, e.g. stacked image sensors
• 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 5/265 : Mixing

Abstract

Systems and methods for implementing array cameras configured to perform super-resolution processing to generate higher resolution super-resolved images using a plurality of captured images and lens stack arrays that can be utilized in array cameras are disclosed. An imaging device in accordance with one embodiment of the invention includes at least one imager array, and each imager in the array comprises a plurality of light sensing elements and a lens stack including at least one lens surface, where the lens stack is configured to form an image on the light sensing elements, control circuitry configured to capture images formed on the light sensing elements of each of the imagers, and a super-resolution processing module configured to generate at least one higher resolution super-resolved image using a plurality of the captured images.

Claims

1. A method for normalizing image data captured by camera arrays, comprising;

obtaining calibration data for an imager array by capturing images using the imager array, where the calibration data indicates mappings between addresses of physical pixels in imagers and logical addresses within an image;

storing the calibration data in a storage device;

normalizing a set of images with respect to an image captured by a baseline imager within the imager array based upon the 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

wherein normalizing a set of images with respect to an image captured by a baseline image comprises;

correcting color differences between the images with respect to an image captured by a baseline imager; and

correcting geometric distortion differences between the captured images with respect to an image captured by a baseline imager.

2. The method of claim 1, wherein normalizing the set of images further comprises:

black level calculation and adjustments;

fixed noise compensation; and

optical point spread function deconvolution.

3. The method of claim 1, further comprising performing temperature normalization using an upstream pipeline processing module based upon a temperature measurement.

4. The method of claim 5, wherein performing temperature normalization comprises performing a refractive index normalization using a stored point spread function selected based upon the temperature measurement.

5. The method of claim 1, wherein correcting color differences between the images further comprises:

obtaining a normalization plane;

fitting a polynomial to the normalization plane by scanning the normalization plane using a space filling curve;

storing fitted polynomials as calibration data; and

correcting color differences between images with respect to an image captured by a baseline imager using the fitted polynomials.

11. The method of claim 1, further comprising detecting and metering parallax using a parallax confirmation and measurement module, where detecting and metering parallax comprises ignoring pixels in the images that are in an exposed occlusion set.

12. The method of claim 1, further comprising:

aligning portions of images captured by different imagers to compensate for parallax using an image pixel correlation module based upon the detected and metered 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, where color information around the edge of the foreground object that is visible to the baseline image and in the occlusion set of the second image is reconstructed in the higher resolution image using the pixels captured by the first image.

16. The method of claim 1, wherein each imager in the array of imagers includes a spectral 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 fillers.

18. The method of claim 1, wherein the imagers in the imager array have different resolutions.

6. The method of claim 6, wherein obtaining a normalization plane comprises:

capturing images of a scene with flat reflectance and calculating a color ratio surface;

removing the black level offset from the pixel values in the captured images; and

low pass filtering the pixel values in the captured images to reduce noise; and

calculating a normalization plane.

8. The method of claim 6, wherein the space filling curve is a spiral space filling curve.

10. The method of claim 6, further comprising:

determining a sensitivity adjustment factor during calibration; and

applying the inverse of the sensitivity adjustment factor when correcting color differences.

7. The method of claim 7, wherein:

the imager array comprises Green imagers that include Green spectral filters including a baseline Green imager, Red imagers that include Red spectral filters, and Blue spectral filters;

a normalization plane is obtained for each of the Red and Blue imagers; and

a normalization plane is calculated for at least one of Red imagers by determining;

Norm R=G(i,j)/(R(i,j)×

(G_center/R_center))

where G is the baseline Green imager, R is a Red imager being normalized with respect to the baseline Green imager, (i, j) describe the pixel position, and G_center, and R_center, are the pixel values at the center position.

9. The method of claim 9, wherein a 6^th order polynomial represented using seven coefficients is fitted to the normalization plane using the space filling curve.

13. The method of claim 13, 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.

15. The method of claim 13, wherein obtaining an image having a resolution that is higher than the resolutions of the images in the set of images comprises performing at least on process selected from the group consisting of:

fusing the aligned image portions;

obtaining an 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.

14. The method of claim 14, wherein the address conversion module, the parallax confirmation and measurement module, the image pixel correlation module, and the super-resolution module are implemented using a general-purpose computer selectively reconfigured by a computer program stored in the computer.

17. The method of claim 17, wherein the baseline imager includes a Green filter.

19. A method for normalizing image data captured by camera arrays, comprising:

obtaining calibration data for imagers in an imager array by capturing images using the imager array, where the calibration data indicates mappings between addresses of physical pixels in imagers and logical addresses within an image;

storing the calibration data in a storage device;

normalizing a set of images with respect to an image captured by a baseline imager within the imager array based upon the 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

wherein obtaining calibration data comprises;

obtaining a normalization plane by;

capturing images of scene with flat reflectance and calculating a color ratio surface;

removing the black level offset from the pixel values in the captured images;

low pass filtering the pixel values in the captured images to reduce noise; and

calculating a normalization plane;

fitting a polynomial to the normalization plane by scanning the normalization plane using a space filling curve; and

storing fitted polynomials as calibration data in the storage device; and

wherein normalizing a set of images with respect to an image captured by a baseline image comprises;

correcting color differences between images with respect to an image captured by a baseline imager using the fitted polynomials; and

correcting geometric distortion differences between the captured images with respect to an image captured by a baseline imager.