Intensity estimation using binary sensor array with spatially varying thresholds

• US 8,420,996 B2
• Filed: 12/23/2009
• Issued: 04/16/2013
• Est. Priority Date: 12/23/2009
• Status: Active Grant

• Current Assignee: Nokia Technologies Oy (Nokia Corporation)
• Original Assignee: Nokia Corporation
• Inventors: Rissa, Tero P., Koskinen, Samu T., Maki-Marttunen, Tuomo, Viikinkoski, Matti
• Primary Examiner(s): Le, Que T
• Assistant Examiner(s): BENNETT, JENNIFER D
• Application Number:
  US12/645,721
• Publication Number:
  US 20110149274A1
• Time in Patent Office: 1,210 Days
• Field of Search: 250/208.1, 250/226, 250/214.R, 250/216, 250/336.1, 348/222.1, 348/234, 348/272, 348/294, 348/297, 257/431, 257/432, 257/440, 257/80, 257/85, 257/257, 257/290, 257/291, 257/292, 257/258
• US Class Current: 250/208.2
• CPC Class Codes: G01J 1/18 using comparison with a reference electric value
• CPC Class Codes: G01J 1/46 using a capacitor
• CPC Class Codes: H04N 25/77 Pixel circuitry, e.g. memories, A/D converters, pixel amplifiers, shared circuits or shared components

Abstract

An apparatus includes an array containing N sub-diffraction limit light sensors each having an associated light absorption activation threshold for switching from a reset state to an activated state, where the light absorption activation values lie within a range of values. The apparatus further includes a processor connected with a memory including computer program code, where the memory and computer program code are configured to, with the processor, cause the apparatus at least to perform estimating an intensity of light that illuminates the array based on electrical outputs of the array.

Claims

1. An apparatus, comprising:

a plurality of sub-diffraction limit light sensors wherein each sensor has an associated activation threshold for switching an output state of the sensor from a first state to a second state and wherein the activation thresholds of at least some of the sensors are defined by different numbers of photons;

a processor; and

a memory including computer program code,

wherein the memory and computer program code are configured to, with the processor, cause the apparatus at least to perform estimating light incident on the plurality of sensors by interpreting the output states of the plurality of sensors in dependence upon a distribution of different activation thresholds of the sensors, wherein the different activation thresholds are defined by different numbers of photons.

2. An apparatus as claimed in claim 1, wherein the sensors are binary sensors having as output states only the first state and the second state.

3. An apparatus as claimed in claim 1, wherein the apparatus is configured to convert a spatial distribution of output states to a spatial distribution of incident light, in dependence upon the distribution of different activation thresholds of the sensors.

4. An apparatus as claimed in claim 1, wherein estimating uses a Bayesian method.

5. An apparatus as claimed in claim 1, wherein the activation thresholds are controlled.

6. An apparatus as claimed in claim 5, wherein the activation thresholds are actively controlled in an electrical manner.

7. An apparatus as claimed in claim 1, wherein the plurality of sensors is arranged in a regular grid.

8. An apparatus as claimed in claim 1, wherein the plurality of sensors comprises more than 10⁶ sensors.

9. An apparatus as claimed in claim 1, embodied as a camera in a user device.

10. A method, comprising:

providing a plurality of sub-diffraction limit light sensors wherein each sensor has an associated activation threshold for switching an output state of the sensor from a first state to a second state and wherein the activation thresholds of at least some of the sensors are defined by different numbers of photons; and

estimating light incident on the plurality of sensors by interpreting the output states of the plurality of sensors in dependence upon a distribution of different activation thresholds of the sensors, wherein the different activation thresholds are defined by different numbers of photons.

11. The method of claim 10, comprising using binary sensors having as output states only the first state and the second state.

12. The method of claim 10, comprising converting a spatial distribution of output states to a spatial distribution of incident light, in dependence upon the distribution of different activation thresholds of the sensors.

13. The method of claim 10, wherein estimating uses a Bayesian method.

14. The method of claim 10, comprising using training data to determine the distribution of different activation thresholds of the sensors, and using electrical outputs of the plurality of sensors and the sensors'"'"' locations to estimate light using the determined distribution of different activation thresholds of the sensors.

15. The method of claim 10, wherein estimating uses the electrical output of the plurality of sensors to determine a number of activated sensors and a probability mass function to determine the distribution of different activation thresholds of the sensors.

16. A non-transitory computer-readable storage medium encoded with instructions that, when executed by a processor, at least perform estimating light incident on a plurality of sensors by interpreting output states of the plurality of sensors in dependence upon a distribution of different activation thresholds of the sensors, wherein the different activation thresholds are defined by different numbers of photons, and each sensor has an associated activation threshold for switching an output state of the sensor from a first state to a second state.

17. The non-transitory computer-readable storage medium as claimed in claim 16, wherein the sensors are binary sensors having as output states only the first state and the second state.

18. The non-transitory computer-readable storage medium as claimed in claim 16, wherein the instructions, when executed, convert a spatial distribution of output states to a spatial distribution of incident light, in dependence upon the distribution of different activation thresholds of the sensors.

19. The non-transitory computer-readable storage medium as claimed in claim 16, wherein estimating uses a Bayesian method.

20. The non-transitory computer-readable storage medium as claimed in claim 16, wherein the instructions, when executed, determine the distribution of different activation thresholds of the sensors.