Abstract
This paper discusses how the vignetting effect of paintings may be transferred to photographs, with attention to center-corner contrast. First, the lightness distribution of both are analyzed. The results show that the painter’s vignette is more complex than that achieved using common digital post-processing methods. It is shown to involve both the 2D and 3D geometry of the scene. Then, an algorithm is developed to transfer the vignetting effect from an example painting to a photograph. The example painting is selected as that has similar contextual geometry with the photograph. The lightness weighting pattern extracted from the selected example painting is adaptively blended with the input photograph to create vignetting effect. In order to avoid over-brightened or over-darkened regions in the enhancement result, the extracted lightness weighting pattern is corrected using a nonlinear curve. A content-aware interpolation method is also proposed to warp the lightness weighting to fit the contextual structure of the photograph. Finally, the local contrast is restored. Experiments show that the proposed algorithm can successfully perform this function. The resulting vignetting effect is more naturally presented with regard to esthetic composition as compared with vignetting achieved with popular software tools and camera models.
Similar content being viewed by others
References
Badrinarayanan V, Kendall A, Cipolla R (2017) Segnet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans Pattern Anal Mach Intell 39(12):2481–2495
Bychkovsky V, Paris S, Chan E, Durand F (2011) Learning photographic global tonal adjustment with a database of input / output image pairs. In: Proceedings of IEEE conference on computer vision and pattern recognition (CVPR), pp 97–104
Chang Y, Saito S, Nakajima M (2007) Example-based colour transformation of image and video using basic colour categories. IEEE Trans Image Process 16(2):329–336
Chen W, Fu Z, Yang D, Deng J (2016) Single-image depth perception in the wild. In: Proceedings of conference on neural information processing systems (NIPS), pp 1–9
Cho H, Lee H, Lee S (2014) Radial bright channel prior for single image vignetting correction. In: Proceedings of European conference computer vision, pp 189–202
Edin R, Jepsen D (2010) Color harmonies: paint watercolors filled with light. North Light Books, an imprint of F + W Media Inc
Farbman Z, Fattal R, Lischinski D, Szeliski R (2008) Edge-preserving decompositions for multi-scale tone and detail manipulation. ACM Trans Graph 27 (3):67:1–67:10
Fattal R, Lischinski D, Werman M (2002) Gradient domain high dynamic range compression. ACM Trans Graph 21(3):249–256
Fiser J, Jamriska O, Simons D, Shechtman E, Lu J, Asente P, Lukac M, Sykora D (2017) Example-based synthesis of stylized facial animations. ACM Trans Graph 36(4):155:1–11
Gatys LA, Ecker AS, Bethge M (2015) A neural algorithm of artistic styleleon. arXiv:1508.06576v2
Goldman DB (2010) Vignette and exposure calibration and compensation. IEEE Trans Pattern Anal Mach Intell 32(12):2276–2288
Gong Y, Sbalzarini IF (2014) Image enhancement by gradient distribution specification. In: Proceedings of ACCV workshops, pp 47–62
Huang H, Zang Y, Li CF (2010) Example-based painting guided by color features. Vis Comput 26:933–942
Huang W, Ding H, Chen G (2017) A novel deep multi-channel residual networks-based metric learning method for moving human localization in video surveillance. Signal Process 142:104–113
Jing Y, Yang Y, Feng Z, Ye J, Song M (2017) Neural style transfer: a review. arXiv:1705.04058v1
Kong S, Shen X, Lin Z, Mech R, Fowlkes C (2016) Photo aesthetics ranking network with attributes and content adaptation, pp 1–24. arXiv:1606.01621v2
Li C, Chen T (2009) Aesthetic visual quality assessment of paintings. IEEE J Sel Top Sign Proces 3(2):236–252
Li J, Yao L, Hendriks E, Wang JZ (2012) Rhythmic brushstrokes distinguish van gogh from his contemporaries: findings via automated brushstroke extraction. IEEE Trans Pattern Anal Mach Intell 34:1159–1176
Liao J, Yao Y, Yuan L, Hua G, Kang SB (2017) Visual attribute transfer through deep image analogy. arXiv:1705.01088v2
Liu Y, Cohen M, Uyttendaele M, Rusinkiewicz S (2014) Autostyle: automatic style transfer from image collections to users’ images. Computer Graphics Forum 33 (4):21–31
Liu G, Yan Y, Ricci E, Yang Y, Han Y, Winkler S, Sebe N (2015) Inferring painting style with multi-task dictionary learning. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence, pp 2162–2168
Long J, Shelhamer E, Darrell T (2014) Fully convolutional networks for semantic segmentation. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), pp 3431–3440
Resales R, Achan K, Frey B (2003) Unsupervised image translation. In: Proceedings of the 9th IEEE international conference on computer vision, pp 472–478
Rigau J, Feixas M, Sbert M (2008) Informational aesthetics measures. IEEE Comput Graph Appl 28(2):24–34
Saleh B, Elgammal A (2015) Large-scale classification of fine-art paintings: learning the right metric on the right feature. arXiv:1505.00855
Samii A, Althoff T (2011) Iterative learning: leveraging the computer as an on-demand expert artist. CS281a Statistical Learning Theory (Michael Jordan and Martin Wainwright) and CS294-69 Image Manipulation and Computational Photography (Maneesh Agrawala), University of California, Berkeley, pp 1–11
Shen X, Hertzmann A, Jia J, Paris S, Price B, Shechtman E, Sachs I (2016) Automatic portrait segmentation for image stylization. Computer Graphics Forum/Proc. of EUROGRAPHICS 36(2):1–10
Strezoski G, Worring M (2017) Omniart: multi-task deep learning for artistic data analysis. arXiv:1708.00684v1
Wang B, Wang W, Yang H, Sun J (2004) Efficient example-based painting and synthesis of 2d directional texture. IEEE Trans Vis Comput Graph 10(3):266–277
Wu J, Zhong S, Jiang J, Yang Y (2017) A novel clustering method for static video summarization. Multimedia Tools and Applications 76(7):9625–9641
Yan C, Zhang Y, Dai F, Zhang J, Li L, Dai Q (2014) Efficient parallel hevc intra-prediction on many-core processor. Electron Lett 50(11):805–806
Yan C, Zhang Y, Xu J, Dai F, Zhang J, Dai Q, Wu F (2014) Efficient parallel framework for hevc motion estimation on many-core processors. IEEE Trans Circuits Syst Video Technol 24(12):2077–2089
Yan C, Xie H, Liu S, Yin J, Zhang Y (2017) Effective uyghur language text detection in complex background images for traffic prompt identification. IEEE Trans Intell Transp Syst 99:1–10
Yan C, Xie H, Yang D, Yin J, Zhang Y, Dai Q (2017) Supervised hash coding with deep neural network for environment perception of intelligent vehicles. IEEE Trans Intell Transp Syst 19(1):284–295
Zhang X, Chan KL, Constable M (2014) Atmospheric perspective effect enhancement of landscape photographs through depth-aware contrast manipulation. IEEE Trans Multimedia 16(3):653–667
Zhang X, Constable M, Chan KL (2014) Exemplar-based portrait photograph enhancement as informed by portrait paintings. Computer Graphics Forum 33(8):38–51
Zhang X, Constable M, Chan KL (2017) Transfer of vignetting effect from paintings to photographs. In: Proceedings of IEEE international conference on acoustics, speech and signal processing (ICASSP)
Zhang X, Constable M, Chan KL, Yu J, Wang J (2017) Computational approaches in the transfer of aesthetic values from paintings to photographs. Springer, Singapore
Zhang X, Kim D, Shen S, Yuan P, Liu S, Tang Z, Zhang G, Zhou X, Gateno J, Liebschner MAK, Xia JJ (2017) An eftd-vp framework for efficiently generating patient-specific anatomically detailed facial soft tissue fe mesh for craniomaxillofacial surgery simulation. Biomech Model Mechanobiol 4:1–16
Zhao MT, Zhu SC (2010) Sisley the abstract painter. In: Proceedings of the 8th international symposium on non-photorealistic animation and rendering, pp 99–107
Zheng Y, Grossman M, Awate S, Gee J (2009) Automatic correction of intensity nonuniformity from sparseness of gradient distribution in medical images. In: Proceedings of the 12th international conference on medical image computing and computer assisted intervention (MICCAI), pp 852–859
Zheng Y, Lin S, Kang SB, Xiao R, Gee JC, Kambhamettu C (2013) Single-image vignetting correction from gradient distribution symmetries. IEEE Trans Pattern Anal Mach Intell 35(6):1480–1494
Zhou B, Zhao H, Puig X, Fidler S, Barriuso A, Torralba A (2017) Scene parsing through ade20k dataset. In: Proceedings of international conference on computer vision and pattern recognition (CVPR), pp 1–9
Zhu Y, Tang G, Zhang X, Jiang J, Tian Q (2018) Haze removal method for natural restoration of images with sky. Neurocomputing 275:499–510
Acknowledgments
This work was supported in part by: (i) the National Natural Science Foundation of China (Grant No. 61602313, 61620106008, and 61602312); (ii) Shenzhen Commission of Scientific Research & Innovations under the Grant No. JCYJ20170302153632883; (iii) Tencent “Rhinoceros Birds” - Scientific Research Foundation for Young Teachers of Shenzhen University; (iv) Research Foundation of Shenzhen University(2016051); (v)Startup Foundation for Advanced Talents, Shenzhen.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zhang, X., Constable, M. & Chan, K.L. Transfer of content-aware vignetting effect from paintings to photographs. Multimed Tools Appl 77, 23851–23875 (2018). https://doi.org/10.1007/s11042-018-5629-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11042-018-5629-x