Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors
Abstract
:1. Introduction
2. Degradation Model
3. Local Consistent Markov Random Fields
3.1. Basic Definition
3.2. Formulation of Local Consistent MRF Model
4. The Solution of Local Consistent MRF
- The input images degraded by haze are normally taken from outdoor natural scenes. Therefore, the scene depth change is usually gradual and the correct depth values of neighboring pixels tend to the same and, hence, the medium transmission map t(x) can be considered as a constant in a small patch, regardless of their scattering coefficient β(x).
- The value variations of A(x) are dependent on the scene depth, that is, objects with the same depth have the same values of A(x). So, the values of A(x) tend to be the same in local except for the pixels at depth discontinuities, whose number is relatively small.
4.1. Construction of Local Consistent MRF Model
4.2. Label Candidates and Initialization
5. Experimental Results
5.1. Qualitative Comparison among Real-World Hazy Images
5.2. Quantitative Comparison
6. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
References
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Image | Entropy | PSNR | ||||
---|---|---|---|---|---|---|
Input | Tan | Ours | Tan | Ours | ||
Figure 6 | Image (1) | 7.0079 | 7.2235 | 7.6043 | 9.9862 | 10.9585 |
Image (2) | 7.0079 | 7.3420 | 7.0079 | 9.3524 | 9.7479 | |
Image (3) | 7.0955 | 7.6129 | 7.7114 | 10.6062 | 12.5667 | |
Image | Input | Nishino | Ours | Nishino | Ours | |
Figure 7 | Image (1) | 7.1143 | 7.6770 | 7.7742 | 10.1835 | 16.3444 |
Image (2) | 7.1578 | 6.9343 | 7.2157 | 15.8571 | 15.9809 | |
Image (3) | 6.5114 | 7.0954 | 7.4754 | 12.1857 | 14.7134 | |
Image | Input | Fattal | Ours | Fattal | Ours | |
Figure 8 | Image (1) | 7.0878 | 7.3270 | 7.4739 | 12.6795 | 15.2639 |
Image (2) | 6.7272 | 6.9164 | 7.6595 | 9.3204 | 12.5983 | |
Image (3) | 7.1773 | 7.2793 | 7.1832 | 16.1457 | 17.5572 | |
Image | Input | He | Ours | He | Ours | |
Figure 9 | Image (1) | 5.6610 | 6.8479 | 7.1597 | 14.4612 | 12.5766 |
Image (2) | 6.4788 | 6.9625 | 6.9819 | 10.6480 | 18.4398 | |
Image (3) | 7.1773 | 7.2793 | 7.1832 | 16.1457 | 17.5572 |
Image | Size | Tan | Ours | |
Figure 6 | Image (1) | 624 × 416 | 21.9913 | 30.5733 |
Image (2) | 596 × 396 | 18.4131 | 25.8991 | |
Image (3) | 1024 × 768 | 53.9185 | 71.4230 | |
Image | Size | Nishino | Ours | |
Figure 7 | Image (1) | 600 × 400 | 29.7421 | 33.8257 |
Image (2) | 465 × 384 | 21.8739 | 26.1618 | |
Image (3) | 440 × 448 | 25.8193 | 29.8271 | |
Image | Size | Fattal | Ours | |
Figure 8 | Image (1) | 512 × 348 | 18.9123 | 25.2018 |
Image (2) | 351 × 244 | 8.0125 | 12.9139 | |
Image (3) | 576 × 768 | 36.9777 | 43.5120 | |
Image | Size | He | Ours | |
Figure 9 | Image (1) | 660 × 440 | 107.9991 | 39.8194 |
Image (2) | 480 × 360 | 84.3372 | 23.7916 | |
Image (3) | 576 × 768 | 181.0391 | 43.5120 |
Input | Size | Caraffa | He | Ours |
---|---|---|---|---|
Figure 10 Image (1) | 640 × 480 | 36.8444 | 102.3404 | 42.4456 |
Figure 10 Image (2) | 1376 × 1032 | 113.3376 | 387.5691 | 125.9364 |
Figure 10 Image (3) | 512 × 384 | 18.9124 | 59.4183 | 24.7468 |
Figure 10 Image (4) | 960 × 720 | 57.1672 | 197.8565 | 69.8188 |
Figure 10 Image (5) | 736 × 552 | 42.3516 | 173.0013 | 58.8756 |
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Qu, C.; Bi, D.-Y.; Sui, P.; Chao, A.-N.; Wang, Y.-F. Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors. Sensors 2017, 17, 2175. https://doi.org/10.3390/s17102175
Qu C, Bi D-Y, Sui P, Chao A-N, Wang Y-F. Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors. Sensors. 2017; 17(10):2175. https://doi.org/10.3390/s17102175
Chicago/Turabian StyleQu, Chen, Du-Yan Bi, Ping Sui, Ai-Nong Chao, and Yun-Fei Wang. 2017. "Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors" Sensors 17, no. 10: 2175. https://doi.org/10.3390/s17102175
APA StyleQu, C., Bi, D.-Y., Sui, P., Chao, A.-N., & Wang, Y.-F. (2017). Robust Dehaze Algorithm for Degraded Image of CMOS Image Sensors. Sensors, 17(10), 2175. https://doi.org/10.3390/s17102175