Abstract
The weather has a detrimental effect on outdoor vision systems and raises the probability of traffic crashes and road accidents. The scattering of atmospheric particles degrades outdoor images captured in poor weather conditions such as haze and fog. The reduced visibility has a significant impact on driving assistance systems designed for automatic vehicles. As a result, clear visibility is critical for outdoor computer vision systems. Image dehazing is one of the ill-posed problems because evaluating transmission depth is challenging. It is essential to estimate transmission depth with the greatest degree of accuracy. In order to estimate or optimize the transmission depth, this paper employs the adaptive Gaussian notch filter and the concept of gamma correction to recover the final scene radiance. The results of the experiments are assessed and compared both quantitatively and qualitatively with state-of-the-art techniques. The experimental results demonstrate that the proposed indicators ensure high consistency in qualitative and quantitative evaluation using six performance metrics: two blind assessment indicators (e, r), contrast gain \((C_{gain})\), visual contrast measure (VCM), peak signal-to-noise ratio (PSNR), structural similarity index measure (SSIM) and probability.
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References
Ancuti CO, Ancuti C, Timofte R (2020) NH-HAZE: An Image Dehazing Benchmark with Non-homogeneous Hazy and Haze-free Images. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 444-445
Ancuti CO, Ancuti C, Timofte R, De Vleeschouwer C (2018) O-haze: A Dehazing Benchmark with Real Hazy and Haze-free Outdoor Images. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp 754-762
Berman D, Avidan S (2016) Non-local image dehazing. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1674-1682
Bi G, Zhang Y, Nie T, Zhang N (2021) Single image dehazing based on haze density estimation in different color spaces. OSA Contin 4(6):1723–1735
Bradley RA, Terry ME. “Rank analysis of incomplete block designs: I. The method of paired comparisons." Biometrika, 39(3/4):324-345
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 25:5187–5198
Choudhary RR, Jisnu KK, Meena G (2020) Image dehazing using deep learning techniques. Proc Comput Sci 167:1110–1119
Cui G, Ma Q, Zhao J, Yang S, Chen Z (2023) Image dehazing algorithm based on optimized dark channel and haze-line priors of adaptive sky segmentation. JOSA A 40(6):1165–1182
Cui T, Qu L, Tian J, Tang Y (2016) Single image haze removal based on luminance weight prior. In: Proceedings of the IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems (CYBER ’16), Chengdu, China
Das SD, Dutta S (2020) Fast deep multi-patch hierarchical network for nonhomogeneous image dehazing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops, pp 482-483
Dhara SK, Roy M, Sen D, Biswas PK (2021) Color cast dependent image dehazing via adaptive airlight refinement and non-linear color balancing. IEEE Trans Circ Syst Video Technol 31:2076–2081
Economopoulos TL, Asvestas PA, Matsopoulos GK (2010) Contrast enhancement of images using partitioned iterated function systems. Image Vis Comput 28:45
Fattal R (2008) Single image dehazing. ACM Trans Graph 27:1
Gao Z, Bai Y (2016) Single image haze removal algorithm using pixel-based airlight constraints. In: Proceedings of the 22nd International Conference on Automation and Computing (ICAC’16): Tackling the New Challenges in Automation and Computing, Colchester, UK
Han X, Sun Q, Li Y, Ye F (2022) A Novel Sonar Image Denoising Algorithm based on Block Matching. In: 2022 International Conference on Microwave and Millimeter Wave Technology (ICMMT) (pp 1-3). IEEE
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33:2341
Hovhannisyan SA, Gasparyan HA, Agaian SS, Ghazaryan A (2021) AED-Net: a single image dehazing. IEEE Access 10:12465–12474
Huang C, Yang D, Zhang R, Wang L, Zhou L (2018) Improved algorithm for image haze removal based on dark channel priority. Comput Electr Eng 70:659–673
Jiang N, Hu K, Zhang T, Chen W, Xu Y, Zhao T (2023) Deep hybrid model for single image dehazing and detail refinement. Pattern Recogn 136:109227
Jobson DJ, Rahman Z-U, Woodell GA, Hines GD (2006) A comparison of visual statistics for the image enhancement of FORESITE aerial images with those of major image classes. In: Proc SPIE
Ju M, Ding C, Guo YJ, Zhang D (2019) IDGCP: image dehazing based on gamma correction prior. IEEE Trans Image Process 29:3104–3118
Ju M, Zhang D, Wang X (2016) Single image dehazing via an improved atmospheric scattering model. The Visual Computer 1
Kumar A, Jha RK, Nishchal NK (2021) An improved Gamma correction model for image dehazing in a multi-exposure fusion framework. J Vis Commun Image Represent 78:103122
Land EH (1986) Recent advances in retinex theory. Vis Res 26:7
Land EH, McCann J (1971) Lightness and retinex theory. J Opt Soc Am A, Opt Image Sci 61:1
Li Z et al (2013) Sparse signal recovery by stepwise subspace pursuit in compressed sensing. Int J Distrib Sensor Netw 9:798537
Li Z et al (2016) Block-based projection matrix design for compressed sensing. Chin J Electron 25:551
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 28:492–505
Li B, Peng X, Wang Z, Xu J, Feng D (Oct. 2017) AOD-Net: All-in-one dehazing network. In: Proc IEEE Int Conf Comput Vis (ICCV), pp 4770-4778
Liu X, Ma Y, Shi Z, Chen J. (Oct. 2019) GridDehazeNet: attention-based multi-scale network for image dehazing. In: Proc IEEE/CVF Int Conf Comput Vis (ICCV), pp 7314-7323
Liu T, Zheng P, Bao J, Chen H (2023) A state-of-the-art survey of welding radiographic image analysis: challenges, technologies and applications. Measurement 214:112821
Lu H, Li Y, Nakashima S, Serikawa S (2016) Single image dehazing through improved atmospheric light estimation. Multimed Tools Appl 75:17081
Meng G, Wang Y, Duan J, Xiang S, Pan C (2013) Efficient image dehazing with boundary constraint and contextual regularization. In: Proceedings of the IEEE Int Conf Comput Vis, pp 617-624
Mi Z, Zhou H, Zheng Y, Wang M (2016) Single image dehazing via multi-scale gradient domain contrast enhancement. IET Image Proc 10:206
Narasimhan SG, Nayar SK (2000) Chromatic framework for vision in bad weather. In: Proceedings of the IEEE Conf Comput Vis Pattern Recognit (CVPR)
Narasimhan SG, Nayar SK (2003) Interactive (DE) weathering of an image using physical models. In: Proceedings of the IEEE Workshop Color Photometric Methods Comput Vis
Narasimhan SG, Nayar SK (2002) Vision and the atmosphere. Int J Comput Vis 48:233
Narasimhan SG, Nayar SK (2003) Contrast restoration of weather degraded images. IEEE Trans Pattern Anal Mach Learn 25:713
Nayar SK, Narasimhan SG (1999) Vision in bad weather. In: Proceedings of the 7th IEEE Int Conf Comput Vis
Okuwobi IP, Ding Z, Wan J, Jiang J (2023) SWM-DE: statistical wavelet model for joint denoising and enhancement for multimodal medical images. Med Novel Technol Dev 18:100234
Patel O, Maravi YPS, Sharma S (2013) A comparative study of histogram equalization based image enhancement techniques for brightness preservation and contrast enhancement. Signal Image Process Int J 4(5):11
Payman M, Masoumzadeh M, Habibi M (2015) A novel adaptive Gaussian restoration filter for reducing periodic noises in digital image. Signal Image Video 9:1179–1191
Pharr M, Humphreys G (2010) Physically based rendering: From theory to implementation. Morgan Kaufmann
Remya RS, Prasad H, Hariharan S, Gopakumar C (2022) Chromosome Image Enhancement for Efficient Karyotyping. In: 2022 International Conference on Innovative Trends in Information Technology (ICITIIT) (pp 1-6). IEEE
Rohaly AM, Corriveau PJ, Libert JM, Webster AA, Baroncini V, Beerends J, Blin JL, Contin L, Hamada T, Harrison D, Hekstra AP (2000) Video quality experts group: Current results and future directions. In: Visual Communications and Image Processing, 4067, pp 742-753, SPIE
Sakaridis C, Dai D, Van Gool L (2018) Semantic foggy scene understanding with synthetic data. Int J Comput Vision 126:973–992
Salazar-Colores S, Cabal-Yepez E, Ramos-Arreguin JM, Botella G, Ledesma-Carrillo LM, Ledesma S (2019) fast image dehazing algorithm using morphological reconstruction. IEEE Trans Image Process 28:2357–2366
Santra S, Chanda B (2015) Single image dehazing with varying atmospheric light intensity. In: Proceedings of the 5th National Conference on Computer Vision, Pattern Recognition, Image Processing and Graphics (NCVPRIPG ’15),
Shao Y, Li L, Ren W, Gao C, Sang N (2020) Domain adaptation for image dehazing. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition, pp 2808-2817
Shiau Y-H et al (2013) Hardware implementation of a fast and efficient haze removal method. IEEE Transactions on Circuits and Systems for Video Technology, 1369
Siddiqua M, Belhaouari SB, Akhter N, Zameer A, Khurshid J (2023) MACGAN: an all-in-one image restoration under adverse conditions using multidomain attention-based conditional GAN. IEEE Access 11:70482–70502
Silberman N, Hoiem D, Kohli P, Fergus R (2012) Indoor segmentation and support inference from rgbd images. In: Computer Vision-ECCV 2012: 12th European Conference on Computer Vision, Florence, Italy, Proceedings, Part V 12 746-760, Springer Berlin Heidelberg
Talwar P, Cecil K (2023) Adaptive filter and EMD based de-noising method of ECG signals: a review. Am J Multidisc Rese Dev (AJMRD) 5(03):09–14
Tan RT (2008) Visibility in bad weather from a single image. In: Proceedings of the IEEE Conf Comput Vis Pattern Recog
Tang Z, Zhang X, Zhang S (2014) Robust perceptual image hashing based on ring partition and NMF. IEEE Trans Knowl Data Eng 26:711
Tang Z, Zhang X, Li X, Zhang S (2016) Robust image hashing with ring partition and invariant vector distance. IEEE Trans Inf Foren Secur 11:200
Tarel J-P, Hautiére N (2009) Fast visibility restoration from a single color or gray level image. In: Proceedings of the 12th IEEE Int Conf Comput Vis
Varghese J, Subhash S, Subramaniam K, Sridhar KP (2020) Adaptive Gaussian notch filter for removing periodic noise from digital images. IET Image Proc 14(8):1529–1538
Wang Z, Bovik AC, Sheikh HR, Simoncelli EP (2004) Image quality assessment: from error visibility to structural similarity. IEEE Trans Image Process 13:600–612
Wang Z, Yang Y, Wang Z, Chang S, Yang J, Huang TS (2015) Learning super-resolution jointly from external and internal examples. IEEE Trans Image Process 24(11):4359–4371
Wang W, Chang F, Ji T, Wu X (2018) A fast single-image dehazing method based on a physical model and gray projection. IEEE Access 6:5641–5653
Yang JS, Jeon SY, Choi JH (2022) Acquisition of a single grid-based phase-contrast X-ray image using instantaneous frequency and noise filtering. Biomed Eng Online 21(1):1–22
Zhang XS, Yang KF, Li YJ (2021) Haze removal with channel-wise scattering coefficient awareness based on grey pixels. Opt Express 29(11):16619–16638
Zhang H, Liu X, Cheung Y (2016) Efficient single image dehazing via scene-adaptive segmentation and improved dark channel model. In: Proceedings of the International Joint Conference on Neural Networks (IJCNN ’16), Vancouver, Canad, Jul
Zhao S, Zhang L, Shen Y, Zhou Y (2021) RefineDNet: a weakly supervised refinement framework for single image dehazing. IEEE Trans Image Process 30:3391–3404
Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24:3522
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, vol 70, pp 1-23, Art no. 5001523, https://doi.org/10.1109/TIM.2020.3024335
Zuiderveld K (1994) Contrast limited adaptive histogram equalization. In: Graphics gems IV. San Diego, USA
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Kumari, A., Sahoo, S.K. An effective and robust single-image dehazing method based on gamma correction and adaptive Gaussian notch filtering. J Supercomput 80, 9253–9276 (2024). https://doi.org/10.1007/s11227-023-05805-z
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DOI: https://doi.org/10.1007/s11227-023-05805-z