Generation of HDR Images in Non-static Conditions based on Gradient Fusion

We present a new method for the generation of HDR images in non-static conditions, i.e. hand held camera and/or dynamic scenes, based on gradient fusion. Given a reference image selected from a set of LDR pictures of the same scene taken with multiple time exposure, our method improves the detail rendition of its radiance map by adding information suitably selected and interpolated from the companion images. The proposed technique is free from ghosting and bleeding, two typical artifacts of HDR images built through image fusion in non-static conditions. The advantages provided by the gradient fusion approach will be supported by the comparison between our results and those of the state of the art.

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

[2]  Steve Mann,et al.  Comparametric equations with practical applications in quantigraphic image processing , 2000, IEEE Trans. Image Process..

[3]  Erik Reinhard,et al.  Ghost Removal in High Dynamic Range Images , 2006, 2006 International Conference on Image Processing.

[4]  Hans-Peter Seidel,et al.  Optimal HDR reconstruction with linear digital cameras , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[5]  Steve Mann,et al.  ON BEING `UNDIGITAL' WITH DIGITAL CAMERAS: EXTENDING DYNAMIC RANGE BY COMBINING DIFFERENTLY EXPOSED PICTURES , 1995 .

[6]  Steve Mann,et al.  On being ` undigital ' with digital cameras : Extending Dynamic Range by CombiningDi erently Exposed , 1995 .

[7]  Antonin Chambolle,et al.  A First-Order Primal-Dual Algorithm for Convex Problems with Applications to Imaging , 2011, Journal of Mathematical Imaging and Vision.

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

[9]  Radim Sára,et al.  A Weak Structure Model for Regular Pattern Recognition Applied to Facade Images , 2010, ACCV.

[10]  Jitendra Malik,et al.  Recovering high dynamic range radiance maps from photographs , 1997, SIGGRAPH '08.

[11]  Thorsten Grosch,et al.  Fast and Robust High Dynamic Range Image Generation with Camera and Object Movement , 2006 .

[12]  Shree K. Nayar,et al.  Radiometric self calibration , 1999, Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149).

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

[14]  Greg Ward,et al.  Automatic High-Dynamic Range Image Generation for Dynamic Scenes , 2008, IEEE Computer Graphics and Applications.

[15]  Richard Szeliski,et al.  High dynamic range video , 2003, ACM Trans. Graph..

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

[17]  Marius Tico,et al.  Artifact-free High Dynamic Range imaging , 2009, 2009 IEEE International Conference on Computational Photography (ICCP).

[18]  Richard Szeliski,et al.  Seamless Image Stitching of Scenes with Large Motions and Exposure Differences , 2006, 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06).

[19]  Patrick Pérez,et al.  Poisson image editing , 2003, ACM Trans. Graph..

[20]  Sang Uk Lee,et al.  Ghost-Free High Dynamic Range Imaging , 2010, ACCV.

[21]  Sylvain Paris,et al.  Error-Tolerant Image Compositing , 2010, International Journal of Computer Vision.