Dense SIFT for ghost-free multi-exposure fusion

Two dense SIFT based quality measures for multi-exposure fusion are presented.A new ghost-free multi-exposure fusion method based on dense SIFT is proposed.Two weight distribution strategies for local contrast extraction are studied. Due to the limited capture range of common imaging sensors, a scene with high dynamic range usually cannot be well described by a single still image because some regions in it may be under-exposed or over-exposed. In this paper, a new multi-exposure fusion method based on dense scale invariant feature transform (SIFT) is presented. In our algorithm, the dense SIFT descriptor is first employed as the activity level measurement to extract local details from source images, and then adopted to remove ghosting artifacts when the captured scene is dynamic with moving objects. Furthermore, two popular weight distribution strategies for local contrast extraction, namely, "weighted-average" and "winner-take-all" are studied in this paper. The effects of these two strategies on the fusion results are compared and discussed. Experimental results demonstrate the effectiveness of the proposed method in terms of both visual quality and objective evaluation.

[1]  Tania Stathaki,et al.  Image Fusion: Algorithms and Applications , 2008 .

[2]  Frédo Durand,et al.  A Fast Approximation of the Bilateral Filter Using a Signal Processing Approach , 2006, ECCV.

[3]  Greg Ward,et al.  Fast, Robust Image Registration for Compositing High Dynamic Range Photographs from Hand-Held Exposures , 2003, J. Graphics, GPU, & Game Tools.

[4]  Wai-kuen Cham,et al.  Gradient-Directed Multiexposure Composition , 2012, IEEE Transactions on Image Processing.

[5]  Hiroshi Nagahashi,et al.  Cross-Parameterization for Triangular Meshes with Semantic Features , 2007 .

[6]  Stavri G. Nikolov,et al.  Image fusion: Advances in the state of the art , 2007, Inf. Fusion.

[7]  E. Reinhard.  Photographic Tone Reproduction for Digital Images , 2002 .

[8]  Jan Kautz,et al.  Exposure Fusion , 2007, 15th Pacific Conference on Computer Graphics and Applications (PG'07).

[9]  B. S. Manjunath,et al.  Multisensor Image Fusion Using the Wavelet Transform , 1995, CVGIP Graph. Model. Image Process..

[10]  Shutao Li,et al.  Fast multi-exposure image fusion with median filter and recursive filter , 2012, IEEE Transactions on Consumer Electronics.

[11]  Kanita Karaduzovi Hadziabdic,et al.  Expert evaluation of deghosting algorithms for multi-exposure high dynamic range imaging , 2014 .

[12]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting (The Morgan Kaufmann Series in Computer Graphics) , 2005 .

[13]  Gemma Piella,et al.  Image Fusion for Enhanced Visualization: A Variational Approach , 2009, International Journal of Computer Vision.

[14]  Mark D. Fairchild,et al.  iCAM06: A refined image appearance model for HDR image rendering , 2007, J. Vis. Commun. Image Represent..

[15]  Michael S. Brown,et al.  Globally Optimized Linear Windowed Tone Mapping , 2010, IEEE Transactions on Visualization and Computer Graphics.

[16]  Jiebo Luo,et al.  Probabilistic Exposure Fusion , 2012, IEEE Transactions on Image Processing.

[17]  Eli Shechtman,et al.  Robust patch-based hdr reconstruction of dynamic scenes , 2012, ACM Trans. Graph..

[18]  Yu Liu,et al.  Multi-focus image fusion with dense SIFT , 2015, Inf. Fusion.

[19]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH.

[20]  A. Ardeshir Goshtasby,et al.  Fusion of multi-exposure images , 2005, Image Vis. Comput..

[21]  Rafal Mantiuk,et al.  Comparison of Deghosting Algorithms for Multi-exposure High Dynamic Range Imaging , 2013, SCCG.

[22]  Miguel Granados,et al.  Automatic noise modeling for ghost-free HDR reconstruction , 2013, ACM Trans. Graph..

[23]  Erik Reinhard,et al.  High Dynamic Range Imaging: Acquisition, Display, and Image-Based Lighting , 2010 .

[24]  Jianbo Shi,et al.  Generalized Random Walks for Fusion of Multi-Exposure Images , 2011, IEEE Transactions on Image Processing.

[25]  Rafael C. González,et al.  Digital image processing using MATLAB , 2006 .

[26]  Anna Tomaszewska,et al.  Image Registration for Multi-exposure High Dynamic Range Image Acquisition , 2007 .

[27]  Changhe Tu,et al.  An exposure fusion approach without ghost for dynamic scenes , 2013, 2013 6th International Congress on Image and Signal Processing (CISP).

[28]  Wai-kuen Cham,et al.  Reference-guided exposure fusion in dynamic scenes , 2012, J. Vis. Commun. Image Represent..

[29]  Richard Szeliski,et al.  Digital photography with flash and no-flash image pairs , 2004, ACM Trans. Graph..

[30]  Zheng Liu,et al.  Objective Assessment of Multiresolution Image Fusion Algorithms for Context Enhancement in Night Vision: A Comparative Study , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Desire Sidibé,et al.  Ghost detection and removal for high dynamic range images: Recent advances , 2012, Signal Process. Image Commun..

[32]  Anup Basu,et al.  QoE-Based Multi-Exposure Fusion in Hierarchical Multivariate Gaussian CRF , 2013, IEEE Transactions on Image Processing.

[33]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Vladimir S. Petrovic,et al.  Subjective tests for image fusion evaluation and objective metric validation , 2007, Inf. Fusion.

[35]  Shutao Li,et al.  Image Fusion With Guided Filtering , 2013, IEEE Transactions on Image Processing.

[36]  Jian Sun,et al.  Guided Image Filtering , 2010, ECCV.

[37]  Matthijs C. Dorst.  Distinctive Image Features from Scale-Invariant Keypoints , 2011 .

[38]  Bo Gu,et al.  Gradient field multi-exposure images fusion for high dynamic range image visualization , 2012, J. Vis. Commun. Image Represent..

[39]  Masahiro Okuda,et al.  Multiple Exposure Fusion for High Dynamic Range Image Acquisition , 2012, IEEE Transactions on Image Processing.

[40]  Nam Ik Cho,et al.  A multi-exposure image fusion algorithm without ghost effect , 2011, 2011 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[41]  Vladimir Petrovic,et al.  Objective image fusion performance measure , 2000 .

[42]  Manuel M. Oliveira,et al.  Domain transform for edge-aware image and video processing , 2011, SIGGRAPH 2011.

[43]  J. Mixter.  Fast , 2012 .