© The Institution of Engineering and Technology
Air pollution is a crucial environmental problem, especially the fine particulate matter (PM2.5) which has become one of the focal points. PM2.5 is a complex pollutant which can intrude the lungs and threaten people's health during the whole lives. In order to enable people to know the PM2.5 index of their surroundings at any time, an image-based PM2.5 predictor with saliency detection (IPPS) is proposed. The proposed predictor first obtains the non-salient regions based on saliency detection technologies. Then, the authors extract two features of the entropy and intensity values of non-salient image saturation map. Finally, they multiply these two features into the approximation of PM2.5 concentration. Experiments show that the proposed IPPS is superior in accuracy and efficiency.
References
-
-
1)
-
9. Gu, K., Lin, W., Zhai, G., et al: ‘No-reference quality metric of contrast-distorted images based on information maximization’, IEEE Trans. Cybern., 2017, 47, (12), pp. 4559–4565 (doi: 10.1109/TCYB.2016.2575544).
-
2)
-
6. Mittal, A., Soundararajan, R., Bovik, A.C.: ‘Making a completely blind image quality analyzer’, Signal Process. Lett., 2013, 20, (3), pp. 209–212 (doi: 10.1109/LSP.2012.2227726).
-
3)
-
6. Gu, K., Qiao, J.-F., Li, X.: ‘Highly efficient picture-based prediction of PM2.5 concentration’, IEEE Trans. Ind. Electron., 2018, .
-
4)
-
1. Sun, J., Sun, J., Xu, Z., et al: ‘Gradient profile prior and its applications in image super-resolution and enhancement’, IEEE Trans. Image Process., 2011, 20, (6), pp. 1529–1542 (doi: 10.1109/TIP.2010.2095871).
-
5)
-
8. Gu, K., Zhou, J., Qiao, J.-F., et al: ‘No-reference quality assessment of screen content pictures’, IEEE Trans. Image Process., 2017, 26, (8), pp. 4005–4018 (doi: 10.1109/TIP.2017.2711279).
-
6)
-
18. Hou, X., Harel, J., Koch, C.: ‘Image signature: highlighting sparse salient regions’, IEEE Trans. Pattern Anal. Mach. Intell., 2012, 34, (1), pp. 194–201 (doi: 10.1109/TPAMI.2011.146).
-
7)
-
10. Gu, K., Tao, D., Qiao, J.-F., et al: ‘Learning a no-reference quality assessment model of enhanced images with big data’, IEEE Trans. Neural Netw. Learn. Syst., 2018, 29, (4), pp. 1301–1313 (doi: 10.1109/TNNLS.2017.2649101).
-
8)
-
13. Gu, K., Zhai, G., Yang, X., et al: ‘Automatic contrast enhancement technology with saliency preservation’, IEEE Trans. Circuits Syst. Video Technol., 2015, 25, (9), pp. 1480–1494 (doi: 10.1109/TCSVT.2014.2372392).
-
9)
-
13. Li, L., Lin, W., Wang, X., et al: ‘No-reference image blur assessment based on discrete orthogonal moments’, Trans. Cybern., 2016, 46, (1), pp. 39–50 (doi: 10.1109/TCYB.2015.2392129).
-
10)
-
12. Gu, K., Zhai, G., Lin, W., et al: ‘No-reference image sharpness assessment in autoregressive parameter space’, Trans. Image Process., 2015, 24, (10), pp. 3218–3231 (doi: 10.1109/TIP.2015.2439035).
-
11)
-
4. Vu, P.V., Chandler, D.M.: ‘A fast wavelet-based algorithm for global and local image sharpness estimation’, Signal Process. Lett., 2012, 19, (7), pp. 423–426, (doi: 10.1109/LSP.2012.2199980).
-
12)
-
31. Vu, N.S., Caplier, A.: ‘Enhanced patterns of oriented magnitudes for face recognition and image matching’, IEEE Trans. Image Process., 2012, 21, (3), pp. 1352–1365 (doi: 10.1109/TIP.2011.2169974).
-
13)
-
9. Zhang, L., Zhang, L., Bovik, A.C.: ‘A feature-enriched completely blind image quality evaluator’, Trans. Image Process., 2015, 24, (8), pp. 2579–2591 (doi: 10.1109/TIP.2015.2426416).
-
14)
-
3. Gu, K., Jakhetiya, V., Qiao, J.-F., et al: ‘Model-based referenceless quality metric of 3D synthesized images using local image description’, IEEE Trans. Image Process., 2018, 27, (1), pp. 394–405 (doi: 10.1109/TIP.2017.2733164).
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