An optimization-based approach to fusion of multi-exposure, low dynamic range images

The problem of compositing a high dynamic range (HDR) image for display on a standard low dynamic range device involves matte-based fusion of multiple images captured with different camera exposures, followed by a suitable tone mapping of the fused HDR image. The fused image should represent the entire scene in a clear, well-exposed manner by bringing the under- and over-exposed regions from the input images into the display range of the device while preserving the local contrast. We define matting as a multi-objective optimization problem based on these desired characteristics of the output, and provide the solution using an Euler-Lagrange technique. The proposed technique yields visually appealing fused images with a high value of contrast. Our technique produces the fused image of a low dynamic range, and thus it eliminates the need for generation of an intermediate HDR image and associated tone mapping. Additionally, our technique does not require any knowledge of the camera response functions or exposure settings.

[1]  John Skilling,et al.  Maximum entropy method in image processing , 1984 .

[2]  John Skilling,et al.  Image restoration by a powerful maximum entropy method , 1982, Comput. Graph. Image Process..

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

[4]  Dani Lischinski,et al.  Gradient Domain High Dynamic Range Compression , 2023 .

[5]  Yung-Yu Chuang,et al.  High dynamic range image reconstruction from hand-held cameras , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[6]  Berthold K. P. Horn,et al.  Shape from shading , 1989 .

[7]  Natasha Gelfand,et al.  Motion-blur-free exposure fusion , 2010, 2010 IEEE International Conference on Image Processing.

[8]  Karol Myszkowski,et al.  Adaptive Logarithmic Mapping For Displaying High Contrast Scenes , 2003, Comput. Graph. Forum.

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

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

[11]  Xinhua Zhuang,et al.  Maximum entropy image reconstruction , 1991, IEEE Trans. Signal Process..

[12]  Edward S. Meinel,et al.  Maximum-entropy image restoration: Lagrange and recursive techniques , 1988 .

[13]  Olivier D. Faugeras,et al.  Shape From Shading , 2006, Handbook of Mathematical Models in Computer Vision.

[14]  Subhasis Chaudhuri,et al.  Bilateral Filter Based Compositing for Variable Exposure Photography , 2009, Eurographics.

[15]  Edward H. Adelson,et al.  Compressing and companding high dynamic range images with subband architectures , 2005, ACM Trans. Graph..

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

[17]  Subhasis Chaudhuri,et al.  A Matte-less, Variational Approach to Automatic Scene Compositing , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

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