METHODS AND SYSTEMS FOR CONTRAST ENHANCEMENT

• US 20100278423A1
• Filed: 04/30/2009
• Published: 11/04/2010
• Est. Priority Date: 04/30/2009
• Status: Abandoned Application

• Current Assignee: Texas Instruments, Inc.
• Original Assignee: Texas Instruments, Inc.
• Inventors: Itoh, Yuji, Arai, Emi
• Application Number:
  US12/433,887
• Publication Number:
  US 20100278423A1
• Time in Patent Office: Days
• US Class Current: 382/169
• CPC Class Codes: G06T 2207/20012 Locally adaptive
• CPC Class Codes: G06T 2207/20021 Dividing image into blocks, subimages or windows
• CPC Class Codes: G06T 5/40 using histogram techniques
• CPC Class Codes: G06T 5/94 based on local image properties, e.g. for local contrast enhancement

Abstract

Methods for contrast enhancement of digital images are provided. A method of adaptive histogram equalization is provided that determines weighting factors for discriminating between sub-regions of a digital image to be more enhanced or less enhanced. Another method for content adaptive local histogram equalization is provided that uses a mapping function in which the dynamic range is not changed by the transformation. A third method for contrast enhancement is provided that includes dividing a digital image into a plurality of regions of pixels, and for each region in the plurality of regions, determining a threshold gray level for the region, generating a mapping curve for the region based on the threshold gray level, and applying the generated mapping curve to each pixel in the region to enhance contrast.

Claims

1. A method for contrast enhancement of a digital image, the method comprising:

dividing a region of pixels in the digital image into a plurality of sub-regions;

determining a weighting factor for each sub-region of the plurality of sub-regions;

generating an accumulated normalized histogram of gray level counts in the region wherein for each sub-region, the weighting factor for the sub-region is applied to gray level counts in the sub-region; and

applying a mapping function based on the accumulated normalized histogram to the pixels in the region to enhance contrast, wherein the mapping function produces an equalized gray level for each pixel in the region.

2. The method of claim 1, wherein generating an accumulated normalized histogram further comprises computing

3. The method of claim 1, wherein determining a weighting factor further comprises computing the weighting factor as a difference between a maximum gray level and a minimum gray level in the sub-region.

4. The method of claim 1, wherein determining a weighting factor further comprises computing the weighting factor based on a variance of gray levels in the sub-region.

5. The method of claim 1, wherein determining a weighting factor further comprises computing the weighting factor based on entropy of the sub-region.

6. The method of claim 1, wherein determining a weighting factor further comprises computing the weighting factor based on at least one gradient of the sub-region.

7. A method for contrast enhancement of a digital image, the method comprising:

dividing a region of pixels in the digital image into a plurality of sub-regions;

generating an accumulated normalized histogram of gray level counts for a sub-region; and

applying a first mapping function based on the accumulated normalized histogram to the pixels in the sub-region to enhance contrast,

wherein the first mapping function changes a gray level of a pixel only when the gray level is between or equal to a maximum gray level and a minimum gray level,

wherein the maximum gray level and the minimum gray level are one selected from a group consisting of a maximum gray level of the sub-region and a minimum gray level of the sub-region, and a weighted average of the maximum gray level of the sub-region and maximum gray levels of neighboring sub-regions and a weighted average of the minimum gray level of the sub-region and minimum gray levels of neighboring sub-regions.

8. The method of claim 7, further comprising:

when the maximum gray level and the minimum gray level are the maximum gray level and the minimum gray level of the sub-region, computing the first mapping function T_j(x) as

9. The method of claim 7, further comprising:

when the accumulated normalized histogram comprises a bimodal distribution comprising first cluster of gray levels and a second cluster of gray levels,

applying a second mapping function based on the accumulated normalized histogram to the pixels having gray levels in the first cluster, wherein the second mapping function changes a gray level of a pixel only when the gray level is between or equal to a maximum gray level of the first cluster and a minimum gray level of the first cluster; and

applying a third mapping function based on the accumulated normalized histogram to the pixels having gray levels in the first cluster, wherein the third mapping function changes a gray level of a pixel only when the gray level is between or equal to a maximum gray level of the second cluster and a minimum gray level of the second cluster.

10. The method of claim 7, wherein the first mapping function is further based on a gain factor for the sub-region, wherein the gain factor g(max_j) is computed as

g(max_j)=(log₁₀(2^β

−

1)−

log₁₀(max_j)+1)

wherein j denotes an index of the sub-region, max_j denotes the maximum gray level of the sub-region, and β

denotes a number of bits per pixel.

11. The method of claim 10, further comprising:

determining an activity factor for the sub-region based on a total number of horizontal gradients in the sub-region and a total number of vertical gradients in the sub-region, and

wherein the first mapping function is further based on the gain factor only when the activity factor exceeds an activity threshold.

12. The method of claim 11, wherein the activity threshold is a relational value between the activity factor of the sub-region and an activity factor of the region, wherein the activity of the region is based on a total number of horizontal gradients in the region and a total number of vertical gradients in the region.

13. The method of claim 12, wherein the relational value is based on an offset factor and is normalized by a normalization factor.

14. The method of claim 9,

wherein the second mapping function is further based on a gain factor gactL for the first cluster, wherein the gain factor gactL is computed as

gactL=((log₁₀(minH_j·

γ

)−

log₁₀(maxL_j))·

RL_j+1

wherein j denotes an index of the sub-region, minH_j denotes the minimum gray level of the second cluster, maxL_j denotes the maximum gray level of the first cluster, β

denotes a number of bits per pixel, and RL_j is an activity threshold of the first cluster, and

wherein the third mapping function is further based on a gain factor gactH for the second cluster, wherein the gain factor gactH is computed as

gactH=((log₁₀((2^β

−

1))−

log₁₀(maxH_j))·

RH_j+1)

wherein RH_j is an activity threshold of the second cluster, and

wherein

15. A method for contrast enhancement of a digital image, the method comprising:

dividing the digital image into a plurality of regions of pixels; and

for each region in the plurality of regions,

determining a threshold gray level for the region;

generating a mapping curve M(x) for the region based on the threshold gray level; and

applying the generated mapping curve to each pixel in the region to enhance contrast.

16. The method of claim 15, wherein determining a threshold gray level further comprises computing the threshold gray level as one half of the sum of the maximum gray level in the region and the minimum gray level in the region.

17. The method of claim 15, wherein the mapping curve M(x) for each pixel x in the region is generated as

18. The method of claim 15, wherein the mapping curve M(x) for each pixel x in the region is generated as

19. The method of claim 15, wherein the mapping curve M(x) for each pixel x in the region is generated as