Abstract
In this research, we tackle image enhancement task both in the traditional and Zero-Shot learning scheme with renovated Laplacian pyramid. Recent image enhancement fields experience power of Zero-Shot learning, estimating output from information of an input image itself without additional ground truth data, aiming for avoiding collection of training dataset and domain shift. As requiring ”zero” training data, introducing effective visual prior is particularly important in Zero-Shot image enhancement. Previous studies mainly focus on designing task specific loss function to capture its internal physical process. On the other, though incorporating signal processing methods into enhancement model is efficaciously performed in supervised learning, is less common in Zero-Shot learning. Aiming for further improvement and adding promising leaps to Zero-Shot learning, this research proposes to incorporate Laplacian pyramid to network process. First, Multiscale Laplacian Enhancement (MLE) is formulated, simply enhancing an input image in the hierarchical Laplacian pyramid representation, resulting in detail enhancement, image sharpening, and contrast improvement depending on its hyper parameters. By combining MLE and introducing visual prior specific to underwater images, Zero-Shot underwater image enhancement model with only seven convolutional layers is proposed. Without prior training and any training data, proposed model attains comparative performance compared with previous state-of-the-art models.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Agustsson, E., Timofte, R.: Ntire 2017 challenge on single image super-resolution: Dataset and study. In: Proceedings of the IEEE conference on computer vision and pattern recognition workshops, pp. 126–135 (2017)
Ancuti, C.O., Ancuti, C., De Vleeschouwer, C., Bekaert, P.: Color balance and fusion for underwater image enhancement. IEEE Trans. Image Process. 27(1), 379–393 (2017)
Anwar, S., Li, C.: Diving deeper into underwater image enhancement: a survey. Signal Process. Image Commun. 89, 115978 (2020). https://doi.org/10.1016/j.image.2020.115978, www.sciencedirect.com/science/article/pii/S0923596520301478
Aubry, M., Paris, S., Hasinoff, S.W., Kautz, J., Durand, F.: Fast local Laplacian filters: theory and applications. ACM Trans. Graph. (TOG) 33(5), 1–14 (2014)
Bojanowski, P., Joulin, A., Lopez-Pas, D., Szlam, A.: Optimizing the latent space of generative networks. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 600–609. PMLR (10–15 Jul 2018), https://proceedings.mlr.press/v80/bojanowski18a.html
Buchsbaum, G.: A spatial processor model for object colour perception. J. Franklin institute 310(1), 1–26 (1980)
Burt, P.J., Adelson, E.H.: The laplacian pyramid as a compact image code. In: Readings in Computer Vision, pp. 671–679. Elsevier (1987)
Cao, X., Rong, S., Liu, Y., Li, T., Wang, Q., He, B.: Nuicnet: non-uniform illumination correction for underwater image using fully convolutional network. IEEE Access 8, 109989–110002 (2020)
Chambolle, A.: An algorithm for total variation minimization and applications. J. Math. Imaging Vis. 20(1), 89–97 (2004)
Chen, L.C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 801–818 (2018)
Cheng, Z., Xiong, Z., Chen, C., Liu, D., Zha, Z.J.: Light field super-resolution with zero-shot learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10010–10019 (2021)
Cho, W., Choi, S., Park, D.K., Shin, I., Choo, J.: Image-to-image translation via group-wise deep whitening-and-coloring transformation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 10639–10647 (2019)
Fu, M., Liu, H., Yu, Y., Chen, J., Wang, K.: DW-GAN: A discrete wavelet transform GAN for nonhomogeneous dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 203–212 (2021)
Gandelsman, Y., Shocher, A., Irani, M.: " double-dip": unsupervised image decomposition via coupled deep-image-priors. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 11026–11035 (2019)
Gao, W., Zhang, X., Yang, L., Liu, H.: An improved sobel edge detection. In: 2010 3rd International Conference on Computer Science and Information Technology, vol. 5, pp. 67–71. IEEE (2010)
Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256. JMLR Workshop and Conference Proceedings (2010)
Goodfellow, I., Bengio, Y., Courville, A.: Deep learning. MIT press (2016)
He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2010)
Islam, M.J., Luo, P., Sattar, J.: Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception. In: Robotics: Science and Systems (RSS). Corvalis, Oregon, USA (July 2020). https://doi.org/10.15607/RSS.2020.XVI.018
Jin, C., Deng, L.J., Huang, T.Z., Vivone, G.: Laplacian pyramid networks: a new approach for multispectral pansharpening. Inf. Fusion 78, 158–170 (2022)
Jinjin, G., Haoming, C., Haoyu, C., Xiaoxing, Y., Ren, J.S., Chao, D.: PIPAL: a large-scale image quality assessment dataset for perceptual image restoration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12356, pp. 633–651. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58621-8_37
Kar, A., Dhara, S.K., Sen, D., Biswas, P.K.: Zero-shot single image restoration through controlled perturbation of koschmieder’s model. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pp. 16205–16215 (2021)
Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. In: ICLR (Poster) (2015)
Lehtinen, J., et al.: Noise2Noise: learning image restoration without clean data. In: Dy, J., Krause, A. (eds.) Proceedings of the 35th International Conference on Machine Learning. Proceedings of Machine Learning Research, vol. 80, pp. 2965–2974. PMLR (10–15 Jul 2018), https://proceedings.mlr.press/v80/lehtinen18a.html
Li, B., Gou, Y., Liu, J.Z., Zhu, H., Zhou, J.T., Peng, X.: Zero-shot image dehazing. IEEE Trans. Image Process. 29, 8457–8466 (2020)
Li, C., Anwar, S., Porikli, F.: Underwater scene prior inspired deep underwater image and video enhancement. Pattern Recogn. 98, 107038 (2020)
Li, C., Guo, C., Ren, W., Cong, R., Hou, J., Kwong, S., Tao, D.: An underwater image enhancement benchmark dataset and beyond. IEEE Trans. Image Process. 29, 4376–4389 (2019)
Li, J., Skinner, K.A., Eustice, R.M., Johnson-Roberson, M.: WaterGAN: unsupervised generative network to enable real-time color correction of monocular underwater images. IEEE Robot. Autom. Lett. 3(1), 387–394 (2017)
M Uplavikar, P., Wu, Z., Wang, Z.: All-in-one underwater image enhancement using domain-adversarial learning. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2019)
McCartney, E.J.: Optics of the atmosphere: scattering by molecules and particles. New York (1976)
Narasimhan, S.G., Nayar, S.K.: Chromatic framework for vision in bad weather. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2000 (Cat. No. PR00662), vol. 1, pp. 598–605. IEEE (2000)
Panetta, K., Gao, C., Agaian, S.: Human-visual-system-inspired underwater image quality measures. IEEE J. Oceanic Eng. 41(3), 541–551 (2015)
Paris, S., Hasinoff, S.W., Kautz, J.: Local Laplacian filters: edge-aware image processing with a Laplacian pyramid. ACM Trans. Graph. 30(4), 68 (2011)
Peli, E.: Contrast in complex images. JOSA A 7(10), 2032–2040 (1990)
Peng, L., Zhu, C., Bian, L.: U-shape transformer for underwater image enhancement. arXiv preprint arXiv:2111.11843 (2021)
Polesel, A., Ramponi, G., Mathews, V.: Image enhancement via adaptive unsharp masking. IEEE Trans. Image Process. 9(3), 505–510 (2000). https://doi.org/10.1109/83.826787
Qin, X., Wang, Z., Bai, Y., Xie, X., Jia, H.: FFA-Net: feature fusion attention network for single image dehazing. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 11908–11915 (2020)
Sharma, A., Tan, R.T.: Nighttime visibility enhancement by increasing the dynamic range and suppression of light effects. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11977–11986 (June 2021)
Ulyanov, D., Vedaldi, A., Lempitsky, V.: Deep image prior. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 9446–9454 (2018)
Wu, H., Liu, J., Xie, Y., Qu, Y., Ma, L.: Knowledge transfer dehazing network for nonhomogeneous dehazing. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (June 2020)
Yang, M., Sowmya, A.: An underwater color image quality evaluation metric. IEEE Trans. Image Process. 24(12), 6062–6071 (2015)
Yoo, J., Uh, Y., Chun, S., Kang, B., Ha, J.W.: Photorealistic style transfer via wavelet transforms. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9036–9045 (2019)
Zhang, L., Zhang, L., Liu, X., Shen, Y., Zhang, S., Zhao, S.: Zero-shot restoration of back-lit images using deep internal learning. In: Proceedings of the 27th ACM International Conference on Multimedia, pp. 1623–1631 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
1 Electronic supplementary material
Below is the link to the electronic supplementary material.
Rights and permissions
Copyright information
© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Takao, S. (2023). Zero-Shot Image Enhancement with Renovated Laplacian Pyramid. In: Karlinsky, L., Michaeli, T., Nishino, K. (eds) Computer Vision – ECCV 2022 Workshops. ECCV 2022. Lecture Notes in Computer Science, vol 13804. Springer, Cham. https://doi.org/10.1007/978-3-031-25069-9_46
Download citation
DOI: https://doi.org/10.1007/978-3-031-25069-9_46
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-25068-2
Online ISBN: 978-3-031-25069-9
eBook Packages: Computer ScienceComputer Science (R0)