Image smog restoration using oblique gradient profile prior and energy minimization

Abstract

Removing the smog from digital images is a challenging pre-processing tool in various imaging systems. Therefore, many smog removal (i.e., desmogging) models are proposed so far to remove the effect of smog from images. The desmogging models are based upon a physical model, it means it requires efficient estimation of transmission map and atmospheric veil from a single smoggy image. Therefore, many prior based restoration models are proposed in the literature to estimate the transmission map and an atmospheric veil. However, these models utilized computationally extensive minimization of an energy function. Also, the existing restoration models suffer from various issues such as distortion of texture, edges, and colors. Therefore, in this paper, a convolutional neural network (CNN) is used to estimate the physical attributes of smoggy images. Oblique gradient channel prior (OGCP) is utilized to restore the smoggy images. Initially, a dataset of smoggy and sunny images are obtained. Thereafter, we have trained CNN to estimate the smog gradient from smoggy images. Finally, based upon the computed smog gradient, OGCP is utilized to restore the still smoggy images. Performance analyses reveal that the proposed CNN-OGCP based desmogging model outperforms the existing desmogging models in terms of various performance metrics.

Keywords

convolutional neural networks / desmogging / smog / oblique gradient channel prior

References

[1] Li C, Guo J, Cong R, Pang Y, Wang B. Underwater image enhancement by dehazing with minimum information loss and histogram distribution prior. IEEE Transactions on Image Processing, 2016, 25(12): 5664–5677

[2] Nnolim U A. Partial differential equation-based hazy image contrast enhancement. Computers & Electrical Engineering, 2018, 72: 670–681

[3] Kim J H, Jang W D, Sim J Y, Kim C S. Optimized contrast enhancement for real-time image and video dehazing. Journal of Visual Communication and Image Representation, 2013, 24(3): 410–425

[4] Kim K, Kim S, Kim K S. Effective image enhancement techniques for fog-affected indoor and outdoor images. IET Image Processing, 2018, 12(4): 465–471

[5] Shi Z, Feng Y, Zhao M, Zhang E, He L. Let you see in sand dust weather: a method based on halo-reduced dark channel prior dehazing for sanddust image enhancement. IEEE Access, 2019, 7: 116722–116733

[6] Lai Y, Chen Y, Chiou C, Hsu C. Single-image dehazing via optimal transmission map under scene priors. IEEE Transactions on Circuits and Systems for Video Technology, 2015, 25(1): 1–14

[7] Bui TM, Kim W. Single image dehazing using color ellipsoid prior. IEEE Transactions on Image Processing, 2018, 27(2): 999–1009

[8] Yu T, Riaz I, Piao J, Shin H. Real-time single image dehazing using blockto-pixel interpolation and adaptive dark channel prior. IET Image Processing, 2015, 9(9): 725–734

[9] Golts A, Freedman D, Elad M. Unsupervised single image dehazing using dark channel prior loss. IEEE Transactions on Image Processing, 2020, 29: 2692–2701

[10] Zhu M, He B, Wu Q. Single image dehazing based on dark channel prior and energy minimization. IEEE Signal Processing Letters, 2018, 25(2): 174–178

[11] Xiao J, Zhu L, Zhang Y, Liu E, Lei J. Scene-aware image dehazing based on sky-segmented dark channel prior. IET Image Processing, 2017, 11(12): 1163–1171

[12] Singh D, Kumar V, Kaur M. Single image dehazing using gradient channel prior. Applied Intelligence, 2019, 49(12): 4276–4293

[13] Bala J, Lakhwani K. Single image desmogging using oblique gradient profile prior and variational minimization. Multidimensional Systems and Signal Processing, 2020, 31: 1259–1275

[14] Zhao D, Xu L, Yan Y, Chen J, Duan L Y. Multi-scale optimal fusion model for single image dehazing. Signal Processing: Image Communication, 2019, 74: 253–265

[15] Jiang Y, Sun C, Zhao Y, Yang L. Image dehazing using adaptive bichannel priors on superpixels. Computer Vision and Image Understanding, 2017, 165: 17–32

[16] Wang Y, Huang T Z, Zhao X L, Deng L J, Ji T Y. A convex single image dehazing model via sparse dark channel prior. Applied Mathematics and Computation, 2020, 375: 125085

[17] Gui B, Zhu Y, Zhen T. Adaptive single image dehazing method based on support vector machine. Journal of Visual Communication and Image Representation, 2020, 70: 102792

[18] Yin S, Wang Y, Yang Y H. A novel image-dehazing network with a parallel attention block. Pattern Recognition, 2020, 102: 107255

[19] Liang Z, Wang Y, Ding X, Mi Z, Fu X. Single underwater image enhancement by attenuation map guided color correction and detail preserved dehazing. Neurocomputing, 2021, 425: 160–172

[20] Zhang J, Wang X, Yang C, Zhang J, He D, Song H. Image dehazing based on dark channel prior and brightness enhancement for agricultural remote sensing images from consumer-grade cameras. Computers and Electronics in Agriculture, 2018, 151: 196–206

[21] Emberton S, Chittka L, Cavallaro A. Underwater image and video dehazing with pure haze region segmentation. Computer Vision and Image Understanding, 2018, 168: 145–156

[22] Xiao J, Shen M, Lei J, Zhou J, Klette R, Sui H. Single image dehazing based on learning of haze layers. Neurocomputing, 2020, 389: 108–122

[23] Guo F, Zhao X, Tang J, Peng H, Liu L, Zou B. Single image dehazing based on fusion strategy. Neurocomputing, 2020, 378: 9–23

[24] Gao Y, Li Q, Li J. Single image dehazing via a dual-fusion method. Image and Vision Computing, 2020, 94: 103868

[25] Khan H, Sharif M, Bibi N, Usman M, Haider S A, Zainab S, Shah J H, Bashir Y, Muhammad N. Localization of radiance transformation for image dehazing in wavelet domain. Neurocomputing, 2020, 381: 141–151

[26] Borkar K, Mukherjee S. Single image dehazing by approximating and eliminating the additional airlight component. Neurocomputing, 2020, 400: 294–308

[27] Galdran A. Image dehazing by artificial multiple-exposure image fusion. Signal Processing, 2018, 149: 135–147

[28] Singh D, Kumar V. A novel dehazing model for remote sensing images. Computers & Electrical Engineering, 2018, 69: 14–27

[29] Yuan F, Zhou Y, Xia X, Shi J, Fang Y, Qian X. Image dehazing based on a transmission fusion strategy by automatic image matting. Computer Vision and Image Understanding, 2020, 194: 102933

[30] Wang W, He C, Xia X G. A constrained total variation model for single image dehazing. Pattern Recognition, 2018, 80: 196–209

[31] Alajarmeh A, Salam R, Abdulrahim K, Marhusin M, Zaidan A, Zaidan B. Real-time framework for image dehazing based on linear transmission and constant-time airlight estimation. Information Sciences, 2018, 436: 108–130

[32] Singh D, Kumar V. Dehazing of outdoor images using notch based integral guided filter. Multimedia Tools and Applications, 2018, 77(20): 27363–27386

[33] Basavegowda H S, Dagnew G. Deep learning approach for microarray cancer data classification. CAAI Transactions on Intelligence Technology, 2020, 5(1): 22–33

[34] Singh D, Kumar V. Single image defogging by gain gradient image filter. Science China Information Sciences, 2019, 62(7): 79101

[35] OsterlandS , Weber J. Analytical analysis of single-stage pressure relief valves. International Journal of Hydromechatronics, 2019, 2(1): 32–53

[36] Wang R, Yu H, Wang G, Zhang G, Wang W. Study on the dynamic and static characteristics of gas static thrust bearing with micro-hole restrictors. International Journal of Hydromechatronics, 2019, 2(3): 189–202

[37] Qi G, Wang H, Haner M, Weng C, Chen S, Zhu Z. Convolutional neural network based detection and judgement of environmental obstacle in vehicle operation. CAAI Transactions on Intelligence Technology, 2019, 4(2): 80–91

[38] Singh D, Kumar V, Kaur M. Image dehazing using window-based integrated means filter. Multimedia Tools and Applications, 2020, 79: 34771–34793

[39] Kaur M, Singh D, Kumar V, Sun K. Color image dehazing using gradient channel prior and guided l0 filter. Information Sciences, 2020, 521: 326–342

[40] Singh D, Kumar V. Image dehazing using moore neighborhood-based gradient profile prior. Signal Processing: Image Communication, 2019, 70: 131–144

[41] Wiens T. Engine speed reduction for hydraulic machinery using predictive algorithms. International Journal of Hydromechatronics, 2019, 2(1): 16–31

[42] Levin A, Lischinski D, Weiss Y. A closed form solution to natural image matting. In: Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition. 2006

[43] Singh D, Kumar V, Kaur M. Classification of COVID-19 patients from chest CT images using multi-objective differential evolution-based convolutional neural networks. European Journal of Clinical Microbiology & Infectious Diseases, 2020, 39(7): 1379–1389

[44] Ding R, Dai L, Li G, Liu H. TDD-net: a tiny defect detection network for printed circuit boards. CAAI Transactions on Intelligence Technology, 2019, 4(2): 110–116

[45] Kaur M, Kumar V. Beta chaotic map based image encryption using genetic algorithm. International Journal of Bifurcation and Chaos, 2018, 28(11): 1850132

[46] Kaur M, Singh D, Uppal R S. Parallel strength pareto evolutionary algorithm-ii based image encryption. IET Image Processing, 2019, 14(6): 1015–1026

[47] Gupta A, Singh D, Kaur M. An efficient image encryption using nondominated sorting genetic algorithm-III based 4-D chaotic maps. Journal of Ambient Intelligence and Humanized Computing, 2020, 11(3): 1309–1324

[48] Kaur M, Singh D, Sun K, Rawat U. Color image encryption using nondominated sorting genetic algorithm with local chaotic search based 5D chaotic map. Future Generation Computer Systems, 2020, 107: 333–350

[49] Kaur M, Kumar V, Li L. Color image encryption approach based on memetic differential evolution. Neural Computing and Applications, 2019, 31(11): 7975–7987

RIGHTS & PERMISSIONS

Higher Education Press