Efficient contrast enhancement using adaptive gamma correction … · 2017. 6. 16. · Gamma correction techniques –Using varying adaptive parameter •Simple form of transform-based

IEEE Transaction on Image Processing

vol. PP, No. 99, 2012

Shi-Chia Huang, Fan-Chieh Cheng, and Yi-Sheng Chiu

Presented by Shu Ran

School of Electrical Engineering and Computer Science

Kyungpook National Univ.

Efficient contrast enhancement using

adaptive gamma correction with

weighting distribution

Abstract

Proposed method

– Automatic modifying histogram and enhancing contrast

– Improving brightness of dimmed images

• Gamma correction

• Probability distribution of luminance pixels

– Enhancing video

• Using temporal information

− Regarding differences between each frame

» Reducing computational complexity

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Introduction

Concept of contrast enhancement

– Improving visual quality

• Computer vision and pattern recognition and image processing

– Circumstance resulting poor contrast

• Lack of operator expertise

• Inadequacy of image capture device

• Environmental conditions in captured scene

− Obscuring details of image or video feature

– Two categories of contrast enhancement

• Direct enhancement

− Directly defining contrast by specific contrast term

» Cannot simultaneously gauging contrast of simple and complex

patterns

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• Indirect enhancement

− Redistributing probability density

» Histogram modifications techniques

Gamma correction techniques

– Using varying adaptive parameter

• Simple form of transform-based gamma correction

− Different images exhibiting same changes in intensity

» Using probability density function

max max/T l l l l

(1)

maxlwhere is the maximum intensity of the input

/lpdf l n MN (2)

where is the number of pixels that have intensity ,

and is the total number of pixels in the image ln l

MN

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» Cumulative distribution function

• Traditional histogram equalization

− Using cdf as transformation curve

• Transformation curves

0

l

k

cdf l pdf k

(3)

maxT l cdf l l (4)

Fig. 1. Transformation curves illustrated by (a) gamma

correction and (b) THE methods, with their corresponding

intensity level modifications shown in (c) and (d). 5/29

Previous works

Approaches of THE and TGC methods

– Earlier works in sub-histograms as 1D histogram

• BBHE calculating mean intensity as threshold value

• DSIHE using median intensity

− RSIHE using multi-equalizations reducing artifacts

− RSWHE using weighting function to smooth sub-histogram

» Losing some statistical information

• BPHEME maximizing entropy of enhanced image via histogram

speciation

– 2D histogram for generating contextual and variational information

• Gaussian mixture model

− Compensating for gray-level distribution of image

• Contextual and variational contrast

− Constructing priori probability

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Proposed method

Proposing hybrid HM method

– Compensating limitations of previous methods

– Creating balance

• Between high levels of visual quality and low computational costs

− Utilizing efficiently combining TGC and THE methods

– Proposing cdf and applied normalized gamma function

• Modifying transformation curve

− Without losing available histogram of statistics

• Proposed adaptive gamma correction

− Increasing low intensity

− Avoiding significant decrement of high intensity

1

max max max max/ /cdf l

T l l l l l l l

(5)

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• Applying weighting distribution function

− Slightly modifying statistical histogram

− Lessen generation of adverse effects

− Modified cdf function

− Sum of

min

max

max min

w

pdf l pdfpdf l pdf

pdf pdf

(6)

where is the adjusted parameter,

is the maximum pdf of statistical histogram,

and is minimum pdf

maxpdf

minpdf

max

0

/l

w w w

k

cdf l pdf l pdf

(7)

wpdf

max

0

l

w w

l

pdf pdf l

(8)

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− Gamma parameter based on cdf

– Using HSV color model for enhancing only luminance intensity

• By proposed AGC and WD method

• Flow chart of AGCWD method

(9) 1w

cdf l

Fig. 2. The flowchart of the AGCWD method.

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– Proposing temporal-based technique

• Reducing computational complexity for video sequence

Fig. 3. The flowchart of the TB method applied to a video sequence.

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• Approximating information content of each frame

max

0

logl

l

H pdf l pdf l

(10)

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Experimental results

Summarizing experimental results

– Image-contrast enhancement

• Enhancing various grayscale and color images

− Following same decomposition of histogram used by previous

– Considering illumination conditions

• Indoor and outdoor scenes

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Visual assessment

– Performance of each method

• Measured by four grayscale images

Fig. 4. The “blackboard” image: (a) is the original image with its

corresponding statistical histogram; the remaining nine images are

the enhancement results with modified histograms generated by the

(b) THE, (c) BBHE, (d) DSIHE, (e) RSIHE, (f) RSWHE, (g) DCRGC,

(h) AWMHE, (i) CVC, and (j) AGCWD methods. 13/29

• Grayscale image of truck

Fig. 5. (a) is the original image with its corresponding statistical

histogram; the remaining nine images are the enhancement results

with modified histograms generated by the (b) THE, (c) BBHE, (d)

DSIHE, (e) RSIHE, (f) RSWHE, (g) DCRGC, (h) AWMHE, (i) CVC,

and (j) AGCWD methods. 14/29

– Grayscale image of viaduct






– Grayscale image of warplane






– Visual assessment for color image

• Color image of man





and (j) AGCWD methods.

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• Color image of square






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• Color image of sky






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• Color image of road






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Quantitative evaluation

– Classifying objective metrics

• Full-reference and no-reference and reduced-reference

– Focusing on FR method

• Highlighting use of distortion-free image

− As reference image for assessment

– Snapshots of environment and devices

• Consumer camera and ColorChecker

• Illumination meter to perform quantitative evaluation

− In specific dark room

Fig. 12. Snapshots of environments and devices.

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• Color information of blocks in ColorChecker

Table 1. The standard RGB(sRGB) values of ColorChecker

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• Capturing images of ColorChecker in low-light condition

Fig. 13. The ColorChecker image: (a) is the original set of images;

the remaining nine sets of images are the enhancement results with

modified histograms generated by the (b) THE, (c) BBHE, (d) DSIHE,

(e) RSIHE, (f) RSWHE, (g) DCRGC, (h) AWMHE, (i) CVC, and (j)

AGCWD methods 23/29

• Quantitative evaluations assessed via AMBE and △E94

Table 2. AMBE assessment of contrast enhancement methods

Table 3. △E94 assessment of contrast enhancement methods

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Enhancement of video sequence

– Provided for comparison

Fig. 14. Seven sampled frames of the “campus” sequence and the

enhancement results generated by each method. 25/29

– Home color video and enhancement results

Fig. 15. Seven sampled frames of the “home” sequence and the

enhancement results generated by each method.

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– Using feature similarity index

• For measuring difference between TB and proposed method

Fig. 16. The FSIM of each frame generated by the TB method for

the (a) “campus” and (b) “home” sequences.

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– Improved rate of frames per second

• By TB method

Table 4. Performance evaluation for the proposed TB method

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Conclusion

Proposed method

– Novel enhancement method for images and video sequence

– Three major steps

• Histogram analysis providing spatial information of single image

− Based on probability and statistical inference

• Using weighting distribution to smooth fluctuant

− Avoiding generation of unfavorable artifacts

• Automatically gamma correction

− Using smooth curve

– Reducing computational time

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Efficient contrast enhancement using adaptive gamma correction … · 2017. 6. 16. · Gamma correction techniques –Using varying adaptive parameter •Simple form of transform-based