TRANSFORMING A DIGITAL IMAGE FROM A LOW DYNAMIC RANGE (LDR) IMAGE TO A HIGH DYNAMIC RANGE (HDR) IMAGE

• US 20120201456A1
• Filed: 07/12/2010
• Published: 08/09/2012
• Est. Priority Date: 10/08/2009
• Status: Active Grant

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: El-Shishiny, Hisham E., El-Mahdy, Ahmed H.
• Granted Patent: US 9,020,257 B2
• Time in Patent Office: Days
• US Class Current: 382/167
• CPC Class Codes: G06T 2200/21 involving computational photography
• CPC Class Codes: G06T 2207/20208 High dynamic range [HDR] image processing
• CPC Class Codes: G06T 5/92 based on global image properties
• CPC Class Codes: H04N 19/14 Coding unit complexity, e.g. amount of activity or edge presence estimation H04N19/146 takes precedence
• CPC Class Codes: H04N 23/71 Circuitry for evaluating the brightness variation
• CPC Class Codes: H04N 23/741 by increasing the dynamic range of the image compared to the dynamic range of the electronic image sensors
• CPC Class Codes: H04N 23/80 Camera processing pipelines; Components thereof
• CPC Class Codes: H04N 5/20 Circuitry for controlling amplitude response
• CPC Class Codes: H04N 9/67 for matrixing camera processing pipelines for matrixing of colour signals H04N23/85

Abstract

The invention provides a method for transforming an image from a Low Dynamic Range (LDR) image obtained with a given camera to a High Dynamic Range (HDR) image, the method comprising:

• • obtaining the exposure-pixel response curve (21) for said given camera
  • converting the LDR image to HSB color space arrays (22), said HSB color space arrays including a Hue array, a Saturation array and a Brigthness array; and
  • determining a Radiance array (23, 24) by inverse mapping each pixel in said Brightness array using the inverse of the exposure-pixel response curve (f-1).

Claims

1-11. -11. (canceled)

12. A method for transforming a digital image from a Low Dynamic Range (LDR) image obtained with a given camera to a High Dynamic Range (HDR) image, comprising:

obtaining an exposure-pixel response curve for the given camera;

converting the LDR image to hue, saturation, and brightness (HSB) color space arrays including a hue array, a saturation array, and a brightness array; and

generating a radiance array by inverse mapping each pixel in the brightness array using an inverse of the exposure-pixel response curve.

13. The method of claim 12, further comprising:

for each pixel in the radiance array determining a local luminance average;

generating an adjusted radiance array by adjusting each pixel of the radiance array using the local luminance average for the pixel; and

converting the LDR image, using the hue array, the saturation array, and the adjusted radiance array, into the HDR image.

14. The method of claim 13, wherein

for each pixel (u,v), the adjusted radiance radiance′

[u,v] is computed according to;

Radiance′

[u,v]=Radiance[u,v]* Radiance[u,v]/Local_Luminance_Average[u,v],

where

Radiance[u,v] designates a radiance value for the pixel (u,v) and Local_Luminance_Average[u,v] designates the local luminance average at the pixel (u,v).

15. The method of claim 13, wherein

the local luminance average is determined from i convolution kernels defined as:

kernel_i[ ]=GaussianKernel(r_i)[ ], where

r_i is a radius of a GaussianKernel and,

i designates a local contrast scale index varying from 0 to 8.

16. The method of claim 15, wherein

r_i is 1/(2*Sqrt(2))*1.6¹.

17. The method of claim 12, wherein

the LDR image is converted according to:

Convert(Hue[ ], Saturation[ ], Radiance[ ]/max(Radiance[ ]))*max(Radiance), where

Hue[ ] designates the hue array,

Saturation[ ] the saturation array, and

Radiance[ ] the radiance array

18. The method of claim 12, wherein

the exposure-pixel response curve is obtained from a camera data sheet of the given camera.

19. The method of claim 12, wherein

the exposure-pixel response curve is obtained using a sequence of differently exposed images from the given camera and for a same screen.

20. A computer hardware system configured to transforms a digital image from a Low Dynamic Range (LDR) image obtained with a given camera to a High Dynamic Range (HDR) image, comprising:

a processor, wherein the processor is configured to perform

obtaining an exposure-pixel response curve for the given camera;

converting the LDR image to hue, saturation, and brightness (HSB) color space arrays including a hue array, a saturation array, and a brightness array; and

generating a radiance array by inverse mapping each pixel in the brightness array using an inverse of the exposure-pixel response curve.

21. The computer hardware system of claim 20, wherein the processor is further configured to perform

for each pixel in the radiance array determining a local luminance average;

generating an adjusted radiance array by adjusting each pixel of the radiance array using the local luminance average for the pixel; and

converting the LDR image, using the hue array, the saturation array, and the adjusted radiance array, into the HDR image.

22. The computer hardware system of claim 21, wherein

for each pixel (u,v), the adjusted radiance radiance′

[u,v] is computed according to;

Radiance′

[u,v]=Radiance[u,v]* Radiance[u,v]/Local Luminance Average[u,v],

where

Radiance[u,v] designates a radiance value for the pixel (u,v) and

Local_Luminance_Average[u,v] designates the local luminance average at the pixel (u,v).

23. The computer hardware system of claim 21, wherein

the local luminance average is determined from i convolution kernels defined as:

kernel_i[ ]=GaussianKernel(r_i)[ ], where

r_i is a radius of a GaussianKernel and,

i designates a local contrast scale index varying from 0 to 8.

24. The computer hardware system of claim 23, wherein

r_i is 1/(2*Sqrt(2))*1.6ⁱ.

25. The computer hardware system of claim 20, wherein

the LDR image is converted according to:

Convert(Hue[ ], Saturation[ ], Radiance[ ]/max(Radiance[ ]))*max(Radiance), where

Hue[ ] designates the hue array,

Saturation[ ] the saturation array, and

Radiance[ ] the radiance array

26. A computer program product comprising a computer usable storage medium having stored therein computer usable program code for transforming a digital image from a Low Dynamic Range (LDR) image obtained with a given camera to a High Dynamic Range (HDR) image, the computer usable program code, which when executed by a computer hardware system, causes the computer hardware system to perform:

obtaining an exposure-pixel response curve for the given camera;

converting the LDR image to hue, saturation, and brightness (HSB) color space arrays including a hue array, a saturation array, and a brightness array; and

generating a radiance array by inverse mapping each pixel in the brightness array using an inverse of the exposure-pixel response curve.

27. The computer program product of claim 26, further comprising:

for each pixel in the radiance array determining a local luminance average;

generating an adjusted radiance array by adjusting each pixel of the radiance array using the local luminance average for the pixel; and

converting the LDR image, using the hue array, the saturation array, and the adjusted radiance array, into the HDR image.

28. The computer program product of claim 27, wherein

for each pixel (u,v), the adjusted radiance radiance′

[u,v] is computed according to;

Radiance′

[u,v] =Radiance[u,v]* Radiance[u,v]/Local_Luminance_Average[u,v],

where

Radiance[u,v] designates a radiance value for the pixel (u,v) and

Local_Luminance_Average[u,v] designates the local luminance average at the pixel (u,v).

29. The computer program product of claim 27, wherein

the local luminance average is determined from i convolution kernels defined as:

kernel_i[ ]=GaussianKernel(r_i)[ ], where

r_i is a radius of a GaussianKernel and,

i designates a local contrast scale index varying from 0 to 8.

30. The computer program product of claim 29, wherein

r_i is 1/(2*Sqrt(2))*1.6ⁱ.

31. The computer program product of claim 26, wherein the LDR image is converted according to:

Convert(Hue[ ], Saturation[ ], Radiance[ ]/max(Radiance[ ]))*max(Radiance), where

Hue[ ] designates the hue array,

Saturation[ ] the saturation array, and

Radiance[ ] the radiance array