Similar articles

Abstract: Haze scatters light transmitted in the air and reduces the visibility of images. Dealing with haze is still a challenge for image processing applications nowadays. For the purpose of haze removal, we propose an accelerated dehazing method based on single pixels. Unlike other methods based on regions, our method estimates the transmission map and atmospheric light for each pixel independently, so that all parameters can be evaluated in one traverse, which is a key to acceleration. Then, the transmission map is bilaterally filtered to restore the relationship between pixels. After restoration via the linear hazy model, the restored images are tuned to improve the contrast, value, and saturation, in particular to offset the intensity errors in different channels caused by the corresponding wavelengths. The experimental results demonstrate that the proposed dehazing method outperforms the state-of-the-art dehazing methods in terms of processing speed. Comparisons with other dehazing methods and quantitative criteria (peak signal-to-noise ratio, detectable marginal rate, and information entropy difference) are introduced to verify its performance.

基于暗通道先验的单幅图像快速去雾算法

[1]Arbelaez P, Maire M, Fowlkes C, et al., 2011. Contour detection and hierarchical image segmentation. IEEE Trans Patt Anal Mach Intell, 33(5):898-916.

[2]Berman D, Treibitz T, Avidan S, 2016. Non-local image dehazing. IEEE Conf on Computer Vision and Pattern Recognition, p.1674-1682.

[3]Chavez PSJr, 1988. An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ, 24(3):459-479.

[4]de Gregorio M, Giordano M, Rossi S, et al., 2016. Experimenting WNN support in object tracking systems. Neurocomputing, 183:79-89.

[5]Deriche R, 1987. Using Canny’s criteria to derive a recursively implemented optimal edge detector. Int J Comput Vis, 1(2):167-187.

[6]Downey JE, Weiss JM, Muelling K, et al., 2016. Blending of brain-machine interface and vision-guided autonomous robotics improves neuroprosthetic arm performance during grasping. J Neuroeng Rehabil, 13, Article 28.

[7]Fattal R, 2008. Single image dehazing. ACM Trans Graph, 27(3), Article 72.

[8]Fattal R, 2014. Dehazing using color-lines. ACM Trans Graph, 34(1), Article 13.

[9]Gastal ESL, Oliveira MM, 2011. Domain transform for edge-aware image and video processing. ACM Trans Graph, 30(4):1-12.

[10]Gibson KB, Nguyen TQ, 2013. An analysis of single image defogging methods using a color ellipsoid framework. EURASIP J Image Video Process, 2013:37.

[11]Gilchrist AL, Jacobsen A, 1983. Lightness constancy through a veiling luminance. J Exp Psychol Hum Perc Perform, 9(6):936-944.

[12]He K, Jian S, Tang X, 2010. Single image haze removal using dark channel prior. IEEE Trans Patt Anal Mach Intell, 33(12):2341-2353.

[13]He K, Sun J, Tang X, 2013. Guided image filtering. IEEE Trans Patt Anal Mach Intell, 35(6):1397-1409.

[14]Karantzalos K, Paragios N, 2009. Recognition-driven two-dimensional competing priors toward automatic and accurate building detection. IEEE Trans Geosci Remote Sens, 47(1):133-144.

[15]Koschmeider H, 1924. Theorie der horizontalen sichtweite. Beitr Phys Freien Atmosph, 12:33-53, 171-181 (in German).

[16]Li CY, Guo JC, 2015. Underwater image enhancement by dehazing and color correction. J Electron Imag, 24(3): 033023.

[17]Magescas F, Prablanc C, 2006. Automatic drive of limb motor plasticity. J Cogn Neurosci, 18(1):75-83.

[18]Narasimhan SG, Nayar SK, 2000. Chromatic framework for vision in bad weather. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.1-8.

[19]Narasimhan SG, Nayar SK, 2002. Vision and the atmosphere. Int J Comput Vis, 48(3):233-254.

[20]Narasimhan SG, Nayar SK, 2003. Contrast restoration of weather degraded images. IEEE Trans Patt Anal Mach Intell, 25(6):713-724.

[21]Nebut C, Fleurey F, Le Traon Y, et al., 2006. Automatic test generation: a use case driven approach. IEEE Trans Softw Eng, 32(3):140-155.

[22]Nishino K, Kratz L, Lombardi S, 2012. Bayesian defogging. Int J Comput Vis, 98(3):263-278.

[23]Schechner YY, Narasimhan SG, Nayar SK, 2001. Instant dehazing of images using polarization. Proc IEEE Conf on Computer Vision and Pattern Recognition, p.1-8.

[24]Schechner YY, Narasimhan SG, Nayar SK, 2003. Polarization-based vision through haze. Appl Opt, 42(3): 511-525.

[25]Shwartz S, Namer E, Schechner YY, 2006. Blind haze separation. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.1-8.

[26]Tan RT, 2008. Visibility in bad weather from a single image. IEEE Computer Society Conf on Computer Vision and Pattern Recognition, p.1-8.

[27]Tomasi C, Manduchi R, 1998. Bilateral filtering for gray and color images. Proc IEEE Int Conf on Computer Vision, p.1-8.

[28]Yang Y, Song YT, Pan HT, et al., 2016. Visual servo simulation of EAST articulated maintenance arm robot. Fus Eng Des, 104:28-33.

[29]Yeo MVM, Li XP, Shen KQ, et al., 2009. Can SVM be used for automatic EEG detection of drowsiness during car driving? Saf Sci, 47(1):115-124.

[30]Zhang H, Parker LE, 2016. CoDe4D: color-depth local spatio-temporal features for human activity recognition from RGB-D videos. IEEE Trans Circ Syst Video Technol, 26(3):541-555.

[31]Zhu QS, Mai JM, Shao L, 2015. A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process, 24(11):3522-3533.