Abstract
Image dehazing has become a critical problem to cater to as it has several parameters that need to be addressed. Real color, contrast, and illumination are the major parameters that are to be restored in the dehazed image. Different scenarios distort these parameters in different ways so it is difficult to restore the original image. Many approaches are used in the literature to cater to these problems but suffer from low contrast, faded color, and weak edges. This article introduces an effective technique, which is named as dark channel spatial stimuli gradient model (DCSSGM) that performs well for the aforementioned problems. The DCSSGM technique applies the dark channel prior (DCP) and spatial stimuli gradient sketch model (SSGSM) on each color channel to eliminate the haze from the image and to restore true edges. SSGSM is responsible to restore robust edges in an image using the perceived brightness and calculations based on neighborhood similarity. Morphology is applied to the resultant image to receive sharp true edges. The final restored image is the output of dynamic histogram equalization (DHE) which restores the contrast of the image. The evaluation analysis qualitatively and quantitatively concludes that the DCSSGM technique outperforms other state-of-the-art image dehazing techniques.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Abdullah-Al-Wadud M, Kabir MH, Dewan MAA, Chae O (2007) A dynamic histogram equalization for image contrast enhancement. IEEE Trans Consum Electron 53(2):593–600
Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22(8):3271–3282
Bansal B, Sidhu JS, Jyoti K (2017) A review of image restoration based image defogging algorithms. Intern J Image Graph Signal Process 9(11):62
Berman D, Avidan S (2016) Non-local image dehazing. In: Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition.
Berman D, Treibitz T, Avidan S (2018) Single image dehazing using haze-lines. In: IEEE transactions on pattern analysis and machine intelligence
Cai B, Xu X, Jia K, Qing C, Tao D (2016) Dehazenet: an end-to-end system for single image haze removal. IEEE Trans Image Process 25(11):5187–5198
Chavez PS Jr (1988) An improved dark-object subtraction technique for atmospheric scattering correction of multispectral data. Remote Sens Environ 24(3):459–479
Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24(11):3888–3901
Fattal R (2008) Single image dehazing. ACM Trans Graph (TOG) 27(3):72
Fu X, Zeng D, Huang Y, Liao Y, Ding X, Paisley J (2016) A fusion-based enhancing method for weakly illuminated images. Signal Process 129:82–96
Gao Y, Su Y, Li Q, Li H, Li J (2019) Single image dehazing via self-constructing image fusion. Signal Process 107284
Hautiere N, Tarel J-P, Aubert D, Dumont E (2011) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereol 27(2):87–95
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33(12):2341–2353
Hodges C, Bennamoun M, Rahmani H (2019) Single image dehazing using deep neural networks. Pattern RecognLett 128:70–77
Kim J-H, Jang W-D, Sim J-Y, Kim C-S (2013) Optimized contrast enhancement for real-time image and video dehazing. J Vis Commun Image Represent 24(3):410–425
Kong NSP, Ibrahim H (2008) Color image enhancement using brightness preserving dynamic histogram equalization. IEEE Trans Consum Electron 54(4):1962–1968
Koschmieder H (1924) Theorie der horizontalenSichtweite. BeitragezurPhysik der freien Atmosphare.33–53
Kratz L, Nishino K (2009) Factorizing scene albedo and depth from a single foggy image. In: 2009 IEEE 12th International Conference on Computer Vision
Li B, Ren W, Fu D, Tao D, Feng D, Zeng W, Wang Z (2019) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28(1):492–505
Li Z, Zheng J (2017) Single image de-hazing using globally guided image filtering. IEEE Trans Image Process 27(1):442–450
Liu Y, Shang J, Pan L, Wang A, Wang M (2019) A unified variational model for single image dehazing. IEEE Access 7:15722–15736
Mathew JJ, James AP (2015) Spatial stimuli gradient sketch model. IEEE Signal Process Lett 22(9):1336–1339
Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Paper presented at the Proceedings of the IEEE international conference on computer vision
Min X, Zhai G, Gu K, Zhu Y, Zhou J, Guo G, Yang X, Guan X, Zhang W (2019) Quality evaluation of image dehazing methods using synthetic hazy images. IEEE Trans Multimedia 21:2319
Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vision 48(3):233–254
Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: Paper presented at the Proceedings of the Seventh IEEE International Conference on Computer Vision
Ren W, Liu S, Zhang H, Pan J, Cao X, Yang MH (2016) Single image dehazing via multi-scale convolutional neural networks. In: Paper presented at the European conference on computer vision
Salazar-Colores S, Cabal-Yepez E, Ramos-Arreguin JM, Botella G, Ledesma-Carrillo LM, Ledesma S (2018) A fast image dehazing algorithm using morphological reconstruction. IEEE Trans Image Process
Schechner YY, Narasimhan SG, Nayar SK (2001) Instant dehazing of images using polarization. Paper presented at the null.
Shwartz S, Namer E, Schechner YY (2006) Blind haze separation. In: 2006 IEEE Computer Society Conference. Paper presented at the Computer Vision and Pattern Recognition
Sun C-C, Ruan S-J, Shie M-C, Pai T-W (2005) Dynamic contrast enhancement based on histogram specification. IEEE Trans Consum Electron 51(4):1300–1305
Tan RT (2008) Visibility in bad weather from a single image. In: Paper presented at the 2008 IEEE Conference on Computer Vision and Pattern Recognition
Tang K, Yang J, Wang J (2014) Investigating haze-relevant features in a learning framework for image dehazing. In: Paper presented at the Proceedings of the IEEE conference on computer vision and pattern recognition
Tarel JP, Hautiere N (2009) Fast visibility restoration from a single color or gray level image. In: 2009 IEEE 12th International Conference. Paper presented at the Computer Vision
Tarel J-P, Hautiere N, Caraffa L, Cord A, Halmaoui H, Gruyer D (2012) Vision enhancement in homogeneous and heterogeneous fog. IEEE IntellTranspSyst Mag 4(2):6–20
Xiao C, Gan J (2012) Fast image dehazing using guided joint bilateral filter. Vis Comput 28(6–8):713–721
Yuan F, Huang H (2018) Image haze removal via reference retrieval and scene prior. IEEE Trans Image Process 27(9):4395–4409
Zhao D, Xu L, Yan Y, Chen J, Duan L-Y (2019) Multi-scale optimal fusion model for single image dehazing. Signal Process Image Commun 74:253–265
Funding
Not applicable.
Author information
Authors and Affiliations
Contributions
All the authors contributed equally to this work.
Corresponding author
Ethics declarations
Conflict of interest
There are no conflicts of interest by all authors.
Research involving human/animals
Not applicable.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yousaf, R.M., Habib, H.A., Mehmood, Z. et al. Image dehazing based on dark channel spatial stimuli gradient model and image morphology. J Ambient Intell Human Comput 12, 8483–8495 (2021). https://doi.org/10.1007/s12652-020-02581-z
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s12652-020-02581-z