A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts
Abstract
:1. Introduction
- Errors in quantitative analyses (e.g., determination of albedo value, surface temperature and vegetation indices);
- Difficulties in comparing multi-temporal data series;
- Challenges in comparing radiometric in situ measurements with values from satellite, aerial or low-altitudes imagery;
- Problems in comparative analyses of spectral signatures in time and/or space;
- A decrease in the accuracy of multispectral imagery classification [17].
1.1. Related Works
1.2. Research Purpose
2. Materials
2.1. Test Area
2.2. Data Acquisition
2.3. Meteorological Conditions
2.3.1. The Synoptic Situation in the Mieruniszki Test Area
2.3.2. The Synoptic Situation in the Kościelisko Test Area
3. Methodology
3.1. Dark Image Prior
3.2. Calculate the Atmospheric Scattering Light
3.3. Estimating Transmission Map
- In the first step, Equation (3) was normalised using atmospheric light . After this operation, the Equation is:In this way, it is possible to normalise each R, G, B band of the image independently.
- According to the adopted assumption that is a constant in a local patch (block) and the value is known, the dark channel can be determined by using the min operator [29]:
- In the next step, it is assumed that for the dehazed image (dark channel) , and value is always positive.
- Based on the above, the transmission value for the block (patch) can be determined from the formula:
3.4. Denoising Images
- In the first step, the filter kernel size was set empirically to 3 × 3 pixels. The average value and variance were determined for the environment of each pixel of the image according to the following formulas:
- In the second step, the square of noise variance is determined for the identified noise waveforms or, in the absence of data, the square of mean-variance from all local neighbourhoods of pixels for the filtered channel (image) is calculated;
4. Results and Quality Assessment
4.1. Quality Assessment
4.1.1. Visual Analysis
4.1.2. Quality Metrics Assessment
PSNR Assessment
RSME Assessment
SSIM Assessment
Universal Quality Index Assessment
Correlation Assessment
Entropy Compare Analysis
A Statistical Significance Test of Results
5. Discussion
6. Conclusions
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s | |
---|---|---|---|---|
Time [s] | 2.43 | 2.36 | 1.75 | 2.45 |
PSNR | Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s |
---|---|---|---|---|
Mean | 26.44 | 23.58 | 17.40 | 21.38 |
Std | 7.41 | 7.24 | 2.60 | 4.39 |
Min | 12.62 | 8.55 | 9.64 | 10.80 |
Max | 44.03 | 45.01 | 24.28 | 33.55 |
RMSE [%] | Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s |
---|---|---|---|---|
Mean | 11.4 | 16.1 | 24.5 | 16.7 |
Std | 8.7 | 14.0 | 7.7 | 8.1 |
Min | 1.1 | 1.1 | 10.6 | 3.6 |
Max | 40.5 | 64.7 | 57.1 | 50.0 |
SSIM | Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s |
---|---|---|---|---|
mean | 0.890 | 0.843 | 0.564 | 0.746 |
std | 0.071 | 0.122 | 0.160 | 0.084 |
min | 0.385 | 0.409 | 0.042 | 0.419 |
max | 0.986 | 0.987 | 0.874 | 0.905 |
Q Index | Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s |
---|---|---|---|---|
mean | 0.881 | 0.828 | 0.546 | 0.642 |
std | 0.081 | 0.167 | 0.192 | 0.128 |
min | 0.533 | 0.275 | 0.019 | 0.265 |
max | 0.991 | 0.990 | 0.850 | 0.896 |
Cross-Correlation | Our Method | He’s et al. | Berman’s et al. | Tarel and Hautiere’s |
---|---|---|---|---|
mean | 0.930 | 0.933 | 0.932 | 0.751 |
std | 0.058 | 0.047 | 0.051 | 0.160 |
min | 0.711 | 0.678 | 0.660 | 0.112 |
max | 0.997 | 0.998 | 0.995 | 0.984 |
Our Method with He’s et al. | Our Method with Berman’s et al. | Our Method with Tarel and Hautiere’s | ||
---|---|---|---|---|
PSNR | p-value | 0.032 | <0.0001 | 0.033 |
z-score | −2.134 | −3.509 | −2.133 | |
RMSE | p-value | 0.048 | <0.0001 | <0.0001 |
z-score | −1.974 | −4.132 | −5.719 | |
SSIM | p-value | 0.039 | <0.0001 | <0.0001 |
z-score | −2.056 | −6.153 | −6.155 | |
Q | p-value | 0.028 | <0.0001 | <0.0001 |
z-score | −2.987 | −6.144 | −6.1539 | |
CC | p-value | <0.0001 | 0.379 | <0.0001 |
z-score | −4.508 | −0.878 | −6.144 |
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Wierzbicki, D.; Kedzierski, M.; Sekrecka, A. A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sens. 2020, 12, 25. https://doi.org/10.3390/rs12010025
Wierzbicki D, Kedzierski M, Sekrecka A. A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sensing. 2020; 12(1):25. https://doi.org/10.3390/rs12010025
Chicago/Turabian StyleWierzbicki, Damian, Michal Kedzierski, and Aleksandra Sekrecka. 2020. "A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts" Remote Sensing 12, no. 1: 25. https://doi.org/10.3390/rs12010025
APA StyleWierzbicki, D., Kedzierski, M., & Sekrecka, A. (2020). A Method for Dehazing Images Obtained from Low Altitudes during High-Pressure Fronts. Remote Sensing, 12(1), 25. https://doi.org/10.3390/rs12010025