Abstract
Sand-dust weather seriously reduces the acquisition effect of computer vision equipment. To solve this problem, this paper proposes a fast sand-dust video quality improvement method using simple color balance and dynamic guided filtering. Our method extracts all frames of the video and then processes each frame in two steps by using the parallel computing method. The first step is to quickly correct the color deviation of the frame by a simple color balance method while eliminating the influence of nonuniform illumination. The second step is to use guided filtering with dynamic adjustment of the penalty coefficient to eliminate the interference of noise to the frame, enhance the contrast and detailed information of the frame, and finally reassemble the processed frames into the video with improved quality. Through qualitative and quantitative comprehensive experiments on sand-dust videos, the experimental results are compared with the existing methods, which prove that our method has advantages in improving the quality of sand-dust videos. The contribution of the proposed method can be summarized as follows: 1) A color balance method combined with screen Poisson equation is proposed. Due to light scattering by sand dust, the illumination of video frames are uneven. Our color balance method can effectively solve the problem that the difference between the target and background is small and difficult to identify when there is insufficient illumination. 2) A strategy of dynamically adjusting the penalty coefficient of guided filtering is proposed. The experimental results show that our method can effectively solve the problem of edge blur in some frames when guided filtering processes sand-dust videos. 3) A method of using multicore parallel processing of video frames is proposed to improve the quality of sand-dust video quickly.
Similar content being viewed by others
Data Availability
The raw/processed data required to reproduce these findings cannot be shared at this time as the data also forms part of an ongoing study.
References
Al-Ameen Z (2016) Visibility enhancement for images captured in dusty weather via Tuned Tri-threshold fuzzy intensification operators. Int J Intell Syst Technol Appl 8(8):10–17
Ancuti CO, Ancuti C, De Vleeschouwer C, Bekaert P (2018) Color balance and fusion for underwater image enhancement. In: IEEE transactions on image processing, vol 27, no 1, pp 379–393. https://doi.org/10.1109/TIP.2017.2759252
Bhandari AK, Srinivas K, Maurya S (2022) Gamma corrected reflectance for low contrast image enhancement using guided filter. Multimed Tools Appl 81:6009–6030. https://doi.org/10.1007/s11042-021-11347-4
Bhat P, Curless B, Cohen M, Lawrence Zitnick C (2008) Fourier Analysis of the 2D Screened Poisson Equation for Gradient Domain Problems. In: Proceedings of the 10th European conference on computer vision: part II (ECCV ’08). Springer, Berlin, Heidelberg, pp 114–128. https://doi.org/10.1007/978-3-540-88688-4_9
Cai B, Xu X, Jia K, Qing C, Tao D (2016) DehazeNet: an end-to-end system for single image haze removal. In: IEEE transactions on image processing, vol 25, no 11, pp 5187–5198. https://doi.org/10.1109/TIP.2016.2598681
Cai B, Ye W, Zhao J (2019) A dynamic texture based segmentation method for ultrasound images with Surfacelet, HMT and parallel computing. Multimed Tools Appl 78:5381–5401. https://doi.org/10.1007/s11042-018-6366-x
Ding X, Wang Y, Zhang J, Fu X (2017) Underwater image dehaze using scene depth estimation with adaptive color correction, OCEANS 2017 - Aberdeen, pp 1–5. https://doi.org/10.1109/OCEANSE.2017.8084665
Fu X, Huang Y, Zeng D, Zhang X-P, Ding X (2014) A fusion-based enhancing approach for single sandstorm image. In: 2014 IEEE 16th international workshop on multimedia signal processing (MMSP), pp 1–5. https://doi.org/10.1109/MMSP.2014.6958791
Gao G, Lai H, Wang L et al (2022) Color balance and sand-dust image enhancement in lab space. Multimed Tools Appl 81:15349–15365. https://doi.org/10.1007/s11042-022-12276-6
Hautière N, Tarel J-P, Didier A, Dumont E (2008) Blind contrast enhancement assessment by gradient ratioing at visible edges. Image Anal Stereology, vol 27. https://doi.org/10.5566/ias.v27.p87-95
He K, Sun J, Tang X (2011) Single Image Haze Removal Using Dark Channel Prior. In: IEEE transactions on pattern analysis and machine intelligence, vol 33, no 12, pp 2341–2353. https://doi.org/10.1109/TPAMI.2010.168
He K, Sun J, Tang X (2013) Guided image filtering. In: IEEE transactions on pattern analysis and machine intelligence, vol 35, no 6, pp 1397–1409. https://doi.org/10.1109/TPAMI.2012.213
He J, Zhang C, Yang R, Zhu K (2016) Convex optimization for fast image dehazing. In: 2016 IEEE international conference on image processing (ICIP), pp 2246–2250. https://doi.org/10.1109/ICIP.2016.7532758
Jiang B, Meng H, Zhao J et al (2018) Single image fog and haze removal based on self-adaptive guided image filter and color channel information of sky region. Multimed Tools Appl 77:13513–13530. https://doi.org/10.1007/s11042-017-4973-6
Kan G et al (2017) A multi-core CPU and many-core GPU based fast parallel shuffled complex evolution global optimization approach. In: IEEE transactions on parallel and distributed systems, vol 28, no 2, pp 332–344, 1. https://doi.org/10.1109/TPDS.2016.2575822
Kuanar S, Mahapatra D, Bilas M et al (2022) Multi-path dilated convolution network for haze and glow removal in nighttime images. Vis Comput 38:1121–1134. https://doi.org/10.1007/s00371-021-02071-z
Kumar A, Jain A (2021) Image smog restoration using oblique gradient profile prior and energy minimization. Front Comput Sci 15:156706. https://doi.org/10.1007/s11704-020-9305-8
Li M, Liu J, Yang W, Sun X, Guo Z (2018) Structure-revealing low-light image enhancement via robust Retinex model. In: IEEE transactions on image processing, vol 27, no 6, pp 2828–2841. https://doi.org/10.1109/TIP.2018.2810539
Limare N, Lisani J-L, Morel J-M, Petro A-B, Sbert C (2011) Simplest color balance. Image processing on line, 1. https://doi.org/10.5201/ipol.2011.llmps-scb
Mittal A, Soundararajan R, Bovik AC (2013) Making a completely blind image quality analyzer. In: IEEE signal processing letters, vol 20, no 3, pp 209–212. https://doi.org/10.1109/LSP.2012.2227726
Morel J-M, Petro AB, Sbert C (2012) 2012 Fourier implementation of Poisson image editing. Pattern Recogn Lett 33(3):342–348. https://doi.org/10.1016/j.patrec.2011.10.010
Morel J-M, Petro A-B, Sbert C (2014) Screened poisson equation for image contrast enhancement. Image Process On Line 4:16–29. https://doi.org/10.5201/ipol.2014.84
Prakash J, Mandal S, Razansky D, Ntziachristos V (2019) Maximum entropy based non-negative optoacoustic tomographic image reconstruction. In: IEEE transactions on biomedical engineering, vol 66, no 9, pp 2604–2616. https://doi.org/10.1109/TBME.2019.2892842
Shi Z, Feng Y, Zhao M, Zhang E, He L (2019) Let You See in Sand Dust Weather: A Method Based on Halo-Reduced Dark Channel Prior Dehazing for Sand-Dust Image Enhancement. In: IEEE Access, vol 7, pp 116722–116733. https://doi.org/10.1109/ACCESS.2019.2936444
Singh H, Kumar A, Balyan LK (2022) Fractional-order Differintegral based multiscale Retinex inspired texture dependent quality enhancement for remotely sensed images. Multimed Tools Appl. https://doi.org/10.1007/s11042-022-13265-5
Srinivas K, Bhandari AK, Kumar PK (2021) A context-based image contrast enhancement using energy equalization with clipping limit. In: IEEE transactions on image processing, vol 30, pp 5391–5401. https://doi.org/10.1109/TIP.2021.3083448
Tang C, von Lukas UF, Vahl M, ET AL (2019) Efficient underwater image and video enhancement based on Retinex. SIViP 13:1011–1018. https://doi.org/10.1007/s11760-019-01439-y
Ullah E, Nawaz R, Iqbal J (2013) Single image haze removal using improved dark channel prior. In: 2013 5th International conference on modelling, identification and control (ICMIC), 2013, pp 245–248
Ullah H et al (2021) Light-DehazeNet: a novel lightweight CNN architecture for single image dehazing. In: IEEE transactions on image processing, vol 30, pp 8968–8982. https://doi.org/10.1109/TIP.2021.3116790
Wang Y, Cai J, Zhang D, Chen X, Wang Y (2022) Nonlinear correction for fringe projection profilometry with shifted-phase histogram equalization. In: IEEE transactions on instrumentation and measurement, vol 71, pp 1–9, Art no. 5005509. https://doi.org/10.1109/TIM.2022.3145361
Wang W, Chen Z, Yuan X, et al (2019) Adaptive Image Enhancement Method for Correcting Low-Illumination Images, vol 496, pp 25–41. https://doi.org/10.1016/j.ins.2019.05.015
Wu X, Kawanishi T, Kashino K (2021) Reflectance-guided histogram equalization and Comparametric approximation. In: IEEE transactions on circuits and systems for video technology, vol 31, no 3, pp 863–876. https://doi.org/10.1109/TCSVT.2020.2991437
Yang D, Sun J (2018) Proximal dehaze-net: a prior learning-based deep network for single image dehazing. In: Ferrari V, Hebert M, Sminchisescu C, Weiss Y (eds) Computer vision – ECCV 2018. ECCV 2018. Lecture notes in computer science(). Springer, Cham, vol 11211. https://doi.org/10.1007/978-3-030-01234-2_43
Yang Y, Zhang C, Liu L et al (2020) Visibility restoration of single image captured in dust and haze weather conditions. Multidimen Syst Signal Process 31(2):619–633. https://doi.org/10.1007/s11045-019-00678-z
Yeh C, Kang L, Lin C, Lin C (2012) Efficient image/video dehazing through haze density analysis based on pixel-based dark channel prior. In: 2012 International conference on information security and intelligent control, pp 238-241. https://doi.org/10.1109/ISIC.2012.6449750
Zhang Z, He H (2021) A customized low-rank prior model for structured cartoon-texture image decomposition. https://doi.org/10.1016/j.image.2021, vol 96, p 116308
Zhu Z, Wei H, Hu G, Li Y, Qi G, Mazur N (2021) A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. In: IEEE transactions on instrumentation and measurement. Art no 5001523, vol 70, pp 1–23. https://doi.org/10.1109/TIM.2020.3024335
Zhu Z, Wei H, Hu G, Li Y, Qi G, Mazur N (2021) A novel fast single image dehazing algorithm based on artificial multiexposure image fusion. In: IEEE transactions on instrumentation and measurement. Art no. 5001523, vol 70, pp 1–23. https://doi.org/10.1109/TIM.2020.3024335
Acknowledgements
This work was supported by the National Science Foundation of China under Grant U1803261, the International Science and Technology Cooperation Project of the Ministry of Education of the People’s Republic of China under grant 2016-2196, and the Excellent doctoral research innovation program of Xinjiang University under Grant XJU2022BS067.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of Interests
The authors declare that they have no conflict of interest.
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ni, D., Jia, Z., Yang, J. et al. A fast sand-dust video quality improvement method using simple color balance and dynamic guided filtering. Multimed Tools Appl 82, 33285–33302 (2023). https://doi.org/10.1007/s11042-023-14991-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-14991-0