Contrast-Controllable Image Enhancement Based on Limited Histogram
Abstract
:1. Introduction
- We devised a histogram segmentation mechanism for the grey-level probability parameter which splits the input images into two sub-images: the main histogram and the restricted histogram. The grey-scale probability parameter is composed of the number of grey-scale pixels among all pixels in the original histogram, and the effects of this varying parameter on the two metrics of contrast and information entropy of the output histogram are elaborated.
- The main histograms are evenly distributed between A and B. Adjusting the size of A and B also changes the output image contrast and average brightness index values. By modifying the main histogram with a uniformly distributed histogram, there will be very few artifacts in the final output image and the brightness of the histogram will become more natural.
- Using the non-linear mapping method given in this paper, the constrained histogram is mapped into the modified master histogram. This aims to reduce the detail loss in the output image, making the main viewing of the enhanced histogram look more detailed and natural.
2. Research Objective and Method
2.1. Research Objective
2.2. Proposed Method
- The first part is the segmentation of the original histogram. Assuming cdfs is the set to a cumulative probability partition value, the corresponding histogram number partition value Ds is calculated cyclically according to Equation (4). For the actual calculation, the initial value DS = 0 is set first, and whether cdf(j) ≥ cdfS is satisfied is judged after cdf(j) is calculated by Equation (5). If the condition is satisfied, then DS = DS + 1; otherwise, the value of DS remains unchanged.
- The second part is the uniform distribution of sub-histogram H1. Suppose [A B] is the histogram grayscale range of the input image, in our schematic A = 0, B = 20; then, the sub-histogram H1 is uniformly distributed in this range; the uniformly distributed histogram is recorded as H1-1. The uniform distribution diagram is shown in Figure 3, and the uniform distribution equation of the histogram is defined as the Equation (7).
- The third part is to map the restricted sub-histogram H2 into the sub-histogram H1-1. Assume i is the index gray value variable of the main histogram H1 and I is the index gray value of restricted histogram H2. The non-linear mapping process consists of 3 main steps. First, to find the grey value DI with the smallest difference in value and its corresponding index value t, we select the grey value I from the sub-histogram H2 and compare it with all the grey values in the H1 histogram, in turn, calculated as shown in Equation (8). Taking the input image histogram H as an example, if we choose I = 3, we find that only the grey value 4 in H1 is closest to 3. At this point, we can determine DI = 4, t = 1. Secondly, we calculate the new grey value It in H1-1 after uniform distribution of grey values according to Equation (9). After the calculation, we find that DI = 4 in H2 has become the new grey value 3 in H1-1. Finally, all DI grey values of the restricted sub-histogram H1 are mapped to the sub-histogram H1-1. In this example, DI = 4 is mapped to It = 3, and Figure 4 represents the final mapping process and results.
3. Evaluation Metrics
- (i)
- Information entropy [31]
- (ii)
- (iii)
- (iv)
- MS-SSIM
4. Experiment Results and Analysis
4.1. Impacts of the Parameters A and B on Brightness and Contrast
4.2. Impacts of cdfs Parameters on Algorithm Performance Metrics
4.3. Comparison of Algorithms
4.3.1. Narrow-Dynamic-Range Image “Tractor” with Low Grayscale
4.3.2. Narrow-Dynamic-Range Image “fish” with Medium Grayscale
4.3.3. Narrow-Dynamic-Range Image “Bridge” with High Grayscale
4.3.4. Average Performance Metric for All Algorithms
5. Experimental Discussions
6. Conclusions
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
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Variable | Histogram Values | |||||||||
---|---|---|---|---|---|---|---|---|---|---|
Gray level | 1 | 3 | 4 | 6 | 7 | 8 | 12 | 16 | 17 | 20 |
Number of pixels | 3 | 4 | 7 | 14 | 7 | 4 | 10 | 12 | 3 | 16 |
Techniques | Entropy (bit) | PSNR (dB) | C (dB) | MS-SSIM |
---|---|---|---|---|
Input image | 5.6278 | — | 31.19 | 1 |
BHEMHE | 5.6254 | 23.97 | 32.07 | 0.8768 |
RMSHE | 5.5856 | 15.92 | 33.81 | 0.8364 |
DSIHE | 5.4967 | 28.21 | 32.43 | 0.9389 |
AGCWD | 5.3453 | 23.58 | 34.34 | 0.9377 |
BHEPL | 5.5817 | 13.44 | 35.84 | 0.6624 |
Ref. [30] | 5.6268 | 12.49 | 38.00 | 0.6477 |
Proposed | 5.6272 | 11.49 | 39.19 | 0.6546 |
Techniques | Entropy (bit) | PSNR (dB) | C (dB) | MS-SSIM |
---|---|---|---|---|
Input image | 5.8827 | — | 42.13 | 1 |
BHEMHE | 5.5462 | 11.79 | 42.29 | 0.4937 |
RMSHE | 5.8231 | 16.86 | 42.14 | 0.6749 |
DSIHE | 5.7467 | 24.62 | 42.66 | 0.8972 |
AGCWD | 5.7231 | 20.30 | 43.56 | 0.9397 |
BHEPL | 5.7756 | 14.71 | 42.69 | 0.5952 |
Ref. [30] | 5.8789 | 16.55 | 43.47 | 0.7744 |
Proposed | 5.8821 | 15.82 | 42.37 | 0.7309 |
Techniques | Entropy (bit) | PSNR (dB) | C (dB) | MS-SSIM |
---|---|---|---|---|
Input image | 5.8731 | ― | 46.76 | 1 |
BHEMHE | 3.8784 | 10.03 | 45.06 | 0.6085 |
RMSHE | 5.7085 | 13.58 | 45.82 | 0.7787 |
DSIHE | 5.6787 | 32.11 | 46.69 | 0.9756 |
AGCWD | 5.4201 | 24.89 | 47.27 | 0.9785 |
BHEPL | 5.5614 | 12.81 | 45.36 | 0.7131 |
Ref. [30] | 5.8470 | 20.95 | 46.60 | 0.9214 |
Proposed | 5.8725 | 8.46 | 42.66 | 0.7899 |
Techniques | Entropy (bit) | PSNR (dB) | C (dB) | MS-SSIM |
---|---|---|---|---|
Input images | 7.1273 | ― | 40.35 | 1 |
BHEMHE | 6.4441 | 18.13 | 40.51 | 0.8358 |
RMSHE | 6.9712 | 24.66 | 40.56 | 0.9272 |
DSIHE | 6.9189 | 19.61 | 41.39 | 0.8653 |
AGCWD | 6.8224 | 14.61 | 43.45 | 0.8872 |
BHEPL | 5.9408 | 16.80 | 41.12 | 0.8164 |
Ref. [30] | 7.1151 | 31.27 | 40.68 | 0.9873 |
Proposed | 7.1266 | 30.90 | 40.74 | 0.9905 |
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Fan, X.; Wang, J.; Wang, H.; Xia, C. Contrast-Controllable Image Enhancement Based on Limited Histogram. Electronics 2022, 11, 3822. https://doi.org/10.3390/electronics11223822
Fan X, Wang J, Wang H, Xia C. Contrast-Controllable Image Enhancement Based on Limited Histogram. Electronics. 2022; 11(22):3822. https://doi.org/10.3390/electronics11223822
Chicago/Turabian StyleFan, Xin, Junyan Wang, Haifeng Wang, and Changgao Xia. 2022. "Contrast-Controllable Image Enhancement Based on Limited Histogram" Electronics 11, no. 22: 3822. https://doi.org/10.3390/electronics11223822
APA StyleFan, X., Wang, J., Wang, H., & Xia, C. (2022). Contrast-Controllable Image Enhancement Based on Limited Histogram. Electronics, 11(22), 3822. https://doi.org/10.3390/electronics11223822