Abstract
Most conventional dehazing methods obtain quality results by solving atmospheric scattering model (ASM) using acquired variables (i.e., global atmospheric light and transmission map). Prior-based strategies have made significant achievements in this task. Nonetheless, they usually obtain unrealistic dehazed images since strong assumptions can barely suit all circumstances. In this paper, we propose a novel image dehazing method with adaptive scattering coefficients to realize visual-friendly and quality-orientated restoration. Specifically, a regional rank-based technique is applied to find the most likely atmospheric light candidate. And then, different from previous image dehazing methods that rely on haze-relevant priors to estimate a transmission map, we develop an image quality-aware approach, together with a dynamic scattering coefficient. In this phase, an optimization function constrained by the image quality-aware indicators is designed to compute the scattering coefficient or transmission. The Fibonacci algorithm is further employed to solve this optimization problem. The proposed method produces high-quality results and exhibits favorable quantitative and qualitative performance compared to related methods.
Similar content being viewed by others
Data Availability Statement
The datasets generated and/or analysed during the current study are available from the corresponding author on reasonable request.
References
Achanta R, Shaji A, Smith K, Lucchi A, Fua P (2012) Slic superpixels compared to state-of-the-art superpixel methods. IEEE Trans Pattern Anal Machi Intell 34:2274–2282
Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22:3271–3282
Ancuti CO, Ancuti C (2013) Single image dehazing by multi-scale fusion. IEEE Trans Image Process 22:3271–3282
Berman D, Treibitz T, Avidan S (2018) Single image dehazing using haze-lines. IEEE Trans Pattern Anal Mach Intell 42:720–734
Berman D, Treibitz T, Avidan S (2018) Single image dehazing using haze-lines. IEEE Trans Pattern Anal Mach Intell 42:720–734
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
Chen ZM, Cui Q, Zhao BR, Song RJ, Zhang XQ, Yoshie O (2022) Sst: Spatial and semantic transformers for multi-label image recognition. IEEE Trans Image Process 31:2570–2583
Chen Z, Wang Y, Yang Y, Liu D (2021) Psd: Principled synthetic-to-real dehazing guided by physical priors. In Proceedings of the IEEE/CVF conference on computer vision and pattern recognition
Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24:3888–3901
Choi LK, You J, Bovik AC (2015) Referenceless prediction of perceptual fog density and perceptual image defogging. IEEE Trans Image Process 24:3888–3901
Cooper TJ, Baqai FA (2004) Analysis and extensions of the frankle-mccann retinex algorithm. J Electron Imaging 13:85 – 92
Cooper TJ, Baqai FA (2004) Analysis and extensions of the frankle-mccann retinex algorithm. J Electron Imaging 13:85–92
Dong Y, Liu YH, Zhang H, Chen SF, Qiao Y (2020) Fd-gan: Generative adversarial networks with fusion-discriminator for single image dehazing. In Proceedings of the AAAI Conference on Artificial Intelligence 34:10729–10736
Dong Z, Jia L, Li H, Long X (2022) Complementary feature enhanced network with vision transformer for image dehazing
Fattal R (2014) Dehazing using color-lines. ACM Trans Graphics (TOG) 34:1–14
Fattal R (2014) Dehazing using color-lines. ACM Trans Graphics (TOG) 34:1–14
Galdran A (2018) Image dehazing by artificial multiple-exposure image fusion. Signal Process 149:135–147
He K, Sun J, Tang X (2011) Single image haze removal using dark channel prior. IEEE Trans Pattern Anal Mach Intell 33:2341–2353
He K, Sun J, Tang X (2012) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35:1397–1409
He K, Sun J, Tang X (2012) Guided image filtering. IEEE Trans Pattern Anal Mach Intell 35:1397–1409
Huang S, Li H, Yang Y, Wang B, Rao N (2020) An end-to-end dehazing network with transitional convolution layer. Multidimensional Syst Signal Process 31:1603–1623
Ju MY, Ding C, Guo YJ, Zhang DY (2019) Idgcp: Image dehazing based on gamma correction prior. IEEE Trans Image Process 29:3104–3118
Ju MY, Ding C, Ren WQ, Yang Y, Zhang DY, Guo YJ (2021) Ide: Image dehazing and exposure using an enhanced atmospheric scattering model. IEEE Trans Image Process 30:2180–2192
Karimi D, Dou HR, Gholipour A (2022) Medical image segmentation using transformer networks. IEEE Access 10:29322–29332
Karimi D, Dou HR, Gholipour A (2022) Medical image segmentation using transformer networks. IEEE Access 10:29322–29332
Kim TK, Paik JK, Kang BS (1998) Contrast enhancement system using spatially adaptive histogram equalization with temporal filtering. IEEE Trans Consumer Electron 44:82–87
Levin A, Lischinski D, Weiss Y (2007) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30:228–242
Levin A, Lischinski D, Weiss Y (2007) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30:228–242
Li BY, Ren WQ, Fu DP, Tao DC, Feng D, Zeng WJ, Wang ZY (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28:492–505
Li D, Yu J, Xiao C (2011) No-reference quality assessment method for defogged images. J Image Graphics 16:1753–1757
Li BY, Ren WQ, Fu DP, Tao DC, Feng D, Zeng WJ, Wang ZY (2018) Benchmarking single-image dehazing and beyond. IEEE Trans Image Process 28:492–505
Li Y, Miao Q, Liu R, Song J, Quan Y, Huang Y (2018) A multi-scale fusion scheme based on haze-relevant features for single image dehazing. Neurocomputing 283:73–86
Liu LX, Liu B, Huang H, Bovik AC (2014) No-reference image quality assessment based on spatial and spectral entropies. Signal Process: Image Commun 29:856–863
Peng YT, Lu Z, Cheng FC, Zheng Y, Huang SC (2019) Image haze removal using airlight white correction, local light. IEEE Trans Circ Syst Video Technol 30:1385–1395
Peng YT, Lu Z, Cheng FC, Zheng Y, Huang SC (2019) Image haze removal using airlight white correction, local light. IEEE Trans Circ Syst Video Technol 30:1385–1395
Qi G, Chang L, Luo Y, Chen Y, Zhu Z, Wang S (2020) A precise multi-exposure image fusion method based on low-level features. Sensors 20
Wang J, Wang W, Wang R, Gao W (2016) Csps: An adaptive pooling method for image classification. IEEE Trans Multimed 18:1000–1010
Yan X, Wang G, Jiang G, Wang Y, Mi Z, Fu X (2022) A natural-based fusion strategy for underwater image enhancement. Multim Tools Appl 81:30051–30068
Yan X, Wang G, Wang G, Wang Y, Fu X (2022) A novel biologically-inspired method for underwater image enhancement. Signal Process Image Commun 104:116670
Yang M, Sowmya A (2015) An underwater color image quality evaluation metric. IEEE Trans Image Process 24:6062–6071
Yan X, Wang G, Jiang G, Wang Y, Mi Z, Fu X (2022) A natural-based fusion strategy for underwater image enhancement. Multim Tools Appl 81:30051–30068
Zhang J, Cao Y, Fang S, Kang Y, Chang WC (2017) Fast haze removal for nighttime image using maximum reflectance prior. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp 7418–7426
Zheng MY, Qi GQ, Zhu ZQ, Li YY, Wei HY, Liu Y (2020) Image dehazing by an artificial image fusion method based on adaptive structure decomposition. IEEE Sensors J 20:8062–8072
Zhu Q, Mai J, Shao L (2015) A fast single image haze removal algorithm using color attenuation prior. IEEE Trans Image Process 24:3522–3533
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. IEEE Trans Instrum Meas 70:1–23
Author information
Authors and Affiliations
Corresponding authors
Ethics declarations
Conflicts of interest
The authors declare that they have no potential 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
Song, C., Liu, S., Yan, X. et al. An image quality-aware approach with adaptive scattering coefficients for single image dehazing. Multimed Tools Appl 83, 25519–25542 (2024). https://doi.org/10.1007/s11042-023-16288-8
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-023-16288-8