Abstract
Nature images make up a significant proportion of the ever growing volume of social media. In this context, automatic and rapid image enhancement is always among the favorable techniques for photographers. Among the image representation models, the Gaussian and Laplacian image pyramids based on isotropic Gaussian kernels were once considered to be inappropriate for image enhancement tasks. The recently proposed Local Laplacian Filter (LLF) updates this view by designing a point-wise intensity remapping process. However, this model filters an image with a consistent strength instead of a dynamical way which takes image contents into account. In this paper, we propose a spatially guided LLF by extending the single-value key parameter into a multi-value matrix that dynamically assigns filtering strengths according to image contents. Since it is still very challenging to recognize arbitrary image contents with machine learning methods, we propose a simple but effective technique, which only approximates the richness of image details instead of specific contents. This trade-off between concrete semantics and algorithm efficiency enables filtering strengths to be spatially guided in the LLF process with little extra computational cost. Experimental results validate our method in terms of visual effects and a conditionally faster LLF implementation.
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References
Bhat P, Zitnick CL, Cohen M, Curless B (2010) Gradientshop: a gradient-domain optimization framework for image and video filtering. ACM Trans Graph 29(2), Article 10
Buades A, Coll B, Morel JM (2006) The stair-casing effect in neighborhood filters and its solution. IEEE Trans Image Process 15(6):1499–1505
Fattal R, Agrawala M, Rusinkiewicz S (2007) Multiscale shape and detail enhancement from multi-light image collections. ACM Trans Graph 26(3), Article 51
Gao Y, Wang M, Ji RR, Wu XD, Dai QH (2014) 3-D object retrieval with hausdorff distance learning. IEEE Trans Ind Electron 61:2088–2098
Gao Y, Wang M, Tao DC, Ji RR, Dai QH (2012) 3-D object retrieval and recognition with hypergraph analysis. IEEE Trans Image Process 21:4290–4303
Gao Y, Wang M, Zha ZJ, Shen JL, Li XL, Wu XD (2013) Visual-textual joint relevance learning for tag-based social image search. IEEE Trans Image Process 22:363–376
Gao Y, Wang M, Zha ZJ, Tian Q, Dai QH, Zhang NY (2011) Less is more: efficient 3-D object retrieval with query view selection. IEEE Trans Multimed 13:1007–1018
Gu HX, Wang Y, Xiang SM, Meng GF, Pan CH (2012) Image guided tone mapping with locally nonlinear model. In: Proceedings of European Conference on Computer Vision
He K, Sun J, Tang XO (2010) Guided image filtering. In: Proceedings of European Conference on Computer Vision
Hong RC, Wang M, Yuan XT, Xu MD, Jiang JG, Yan SC, Chua TS (2011) Video accessibility enhancement for hearing-impaired users. ACM Trans Multimed Comput, Commun, Appl 7: Article 24
Li HJ, Tang JH, Wu S, Zhang YD, Lin SX (2010) Automatic detection and analysis of player action in moving background sports video sequences. IEEE Trans Circ Syst Video Technol 20:351–364
Li HJ, Yi L, Tang JH, Wang XH (2011) Capturing a great photo via learning from community-contributed photo collections. In: Proceedings of ACM Multimedia
Ling Y, Yan CP, Liu CX, Wang X (2012) Adaptive tone-preserved image detail enhancement. Vis Comput 28:733–742
Ni BB, Xu MD, Cheng B, Wang M, Yan SC, Tian Q (2013) Learning to photograph: a compositional perspective. IEEE Trans Multimed 15(5):1138–1151
Paris S, Hasinoff SW, Kautz J (2011) Local Laplacian Filters: edge-aware image processing with a Laplacian pyramid. In: Proceedings of ACM SIGGRAPH
Paris S, Kornprobst P, Tumblin J, Durand F (2009) Bilateral filtering: theory and applications. Found Trends Comput Graph Vis
Perona P, Malik J (1990) Scale-space and edge detection using anisotropic diffusion. IEEE Trans Pattern Anal Mach Intell 12(7):629–639
Wang M, Hua XS (2011) Active learning in multimedia annotation and retrieval: a survey. ACM Trans Intell Syst Technol 2:10–31
Wang M, Hua XS, Hong RC, Tang JH, Qi GJ, Song Y (2009) Unified video annotation via multigraph learning. IEEE Trans Circ Syst Video Technol 19:733–746
Wang M, Ni BB, Hua XS, Chua TS (2012) Assistive tagging: a survey of multimedia tagging with human-computer joint exploration. ACM Comput Surv 44(4): Article 25
Wang M, Yang KY, Hua XS, Zhang HJ (2010) Towards a relevant and diverse search of social images. IEEE Trans Multimed 12:829–842
Yang KY, Hua XS, Wang M, Zhang HJ (2011) Tag tagging: towards more descriptive keywords of image content. IEEE Trans Multimed 13(4):662–673
Acknowledgments
The authors sincerely appreciate the useful comments and suggestions from the anonymous reviewers. This work was supported by National Natural Science Fund of China (Grant No. 61301222), China Postdoctoral Science Foundation (Grant No. 2013M541821), Fundamental Research Funds for the Central Universities (Grant No. 2013HGQC0018, 2013HGBH0027, 2013HGBZ0166)
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Hao, S., Wang, M., Hong, R. et al. Spatially guided local Laplacian filter for nature image detail enhancement. Multimed Tools Appl 75, 1529–1542 (2016). https://doi.org/10.1007/s11042-014-2058-3
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DOI: https://doi.org/10.1007/s11042-014-2058-3