Efficient contrast enhancement using adaptive gamma correction … · 2017. 6. 16. · Gamma...
Transcript of Efficient contrast enhancement using adaptive gamma correction … · 2017. 6. 16. · Gamma...
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
Fig. 6. (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. 15/29
– Grayscale image of warplane
Fig. 7. (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. 16/29
– Visual assessment for color image
• Color image of man
Fig. 8. (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.
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• Color image of square
Fig. 9. (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.
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• Color image of sky
Fig. 10. (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.
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• Color image of road
Fig. 11. (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.
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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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