Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering
Abstract
:1. Introduction
2. Materials and Methods
2.1. Multi-Scale Guided Filtering
2.2. Enhancement of the Base Layer Based on CLAHE
- Segment the original image into non-overlapping, equal-sized blocks.
- Calculate the histogram for each block separately.
- Compute the clipping limit
- 4.
- Clip the histogram and distribute the pixels. In each of the segmented blocks, clip the histogram according to the clipping limit, and then distribute the number of pixels clipped from each gray level evenly, as shown in Figure 4.
- 5.
- Performed histogram equalization on each sub-block after the pixels have been redistributed.
- 6.
- To avoid block artifacts in the processed image, carry out interpolation calculations to determine the values of pixels in each sub-block, as shown in Figure 5.
2.3. Detail Layer Enhancement Based on Dynamic Linear Enhancement
- (1)
- Average brightness calculation. Calculate the average intensity of the input image . The calculation formula is as follows:
- (2)
- Contrast Adjustment. Adjust the contrast of the image using the contrast factor If the contrast factor is greater than 1, the contrast is enhanced. If the contrast factor is less than 1, the contrast is reduced. The adjusted image is calculated as follows:
- (3)
- Return the adjusted image. After the above steps, an image with adjusted contrast is obtained, where the intensity of each pixel has been dynamically linearly transformed based on the contrast factor provided by the user, offering a certain degree of flexibility.
2.4. Image Fusion
3. Results
- Peak Signal-to-Noise Ratio
- Information Entropy
- Average Gradient
3.1. Subjective Evaluation
3.2. Objective Assessment
4. Discussion
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Number | Algorithms | PSNR | IE | AG | PIQE |
---|---|---|---|---|---|
Scene One | HE | 13.75 | 6.95 | 86.20 | 38.79 |
GF&DDE | 23.42 | 6.93 | 48.72 | 38.91 | |
BF&DDE | 27.07 | 7.05 | 49.22 | 66.21 | |
Our algorithm | 27.07 | 7.34 | 54.15 | 34.45 | |
Scene Two | HE | 12.33 | 5.91 | 61.83 | 48.54 |
GF&DDE | 22.83 | 6.09 | 18.43 | 11.32 | |
BF&DDE | 19.56 | 6.11 | 19.48 | 42.97 | |
Our algorithm | 21.20 | 6.47 | 19.84 | 9.35 | |
Scene Three | HE | 11.80 | 5.66 | 86.92 | 46.18 |
GF&DDE | 18.57 | 5.70 | 15.88 | 9.38 | |
BF&DDE | 21.46 | 5.79 | 15.79 | 47.56 | |
Our algorithm | 30.74 | 6.16 | 19.05 | 8.02 |
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Li, H.; Wang, S.; Li, S.; Wang, H.; Wen, S.; Li, F. Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering. Fire 2024, 7, 192. https://doi.org/10.3390/fire7060192
Li H, Wang S, Li S, Wang H, Wen S, Li F. Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering. Fire. 2024; 7(6):192. https://doi.org/10.3390/fire7060192
Chicago/Turabian StyleLi, Huaizhou, Shuaijun Wang, Sen Li, Hong Wang, Shupei Wen, and Fengyu Li. 2024. "Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering" Fire 7, no. 6: 192. https://doi.org/10.3390/fire7060192
APA StyleLi, H., Wang, S., Li, S., Wang, H., Wen, S., & Li, F. (2024). Thermal Infrared-Image-Enhancement Algorithm Based on Multi-Scale Guided Filtering. Fire, 7(6), 192. https://doi.org/10.3390/fire7060192