Gradient field multi-exposure images fusion for high dynamic range image visualization

This paper presents a novel method for fusing multi-exposure images into a low dynamic range (LDR) image that is suitable for display and visualization but it contains details in the high dynamic range (HDR) counterpart. Fused gradient field is derived from the structure tensor of inputs based on multi-dimensional Riemannian geometry with a Euclidean metric assumed. Afterwards, a new method is proposed for modifying the gradient field iteratively with twice average filtering and nonlinearly compressing in multi-scales. These modification operations are all done at the finest resolution. The result is obtained through solving a Poisson equation then linearly stretching to the common range. Experimental results demonstrate the efficiency and effectiveness of this method.

[1]  Y. Shimodaira,et al.  Gradient Based Synthesized Multiple Exposure Time HDR Image , 2007, 2007 IEEE Instrumentation & Measurement Technology Conference IMTC 2007.

[2]  Aldo Cumani,et al.  Edge detection in multispectral images , 1991, CVGIP Graph. Model. Image Process..

[3]  Sebastiano Battiato,et al.  High dynamic range imaging for digital still camera: an overview , 2003, J. Electronic Imaging.

[4]  Jan Kautz,et al.  Exposure Fusion , 2009, 15th Pacific Conference on Computer Graphics and Applications (PG'07).

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

[6]  Robert L. Stevenson,et al.  Estimation-theoretic approach to dynamic range enhancement using multiple exposures , 2003, J. Electronic Imaging.

[7]  William L. Briggs,et al.  A multigrid tutorial , 1987 .

[8]  Kin-Man Lam,et al.  An adaptive algorithm for the display of high-dynamic range images , 2007, J. Vis. Commun. Image Represent..

[9]  Katsushi Ikeuchi,et al.  Estimating camera response functions using probabilistic intensity similarity , 2008, 2008 IEEE Conference on Computer Vision and Pattern Recognition.

[10]  E. Dermatas,et al.  Non-parametric estimation of camera function , 2006, MELECON 2006 - 2006 IEEE Mediterranean Electrotechnical Conference.

[11]  Stephen Lin,et al.  Determining the radiometric response function from a single grayscale image , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

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

[13]  Stephen Lin,et al.  Radiometric Calibration from Noise Distributions , 2007, 2007 IEEE Conference on Computer Vision and Pattern Recognition.

[14]  Joachim Weickert,et al.  Anisotropic diffusion in image processing , 1996 .

[15]  Wai-kuen Cham,et al.  Gradient-directed composition of multi-exposure images , 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

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

[17]  Joel Spruck,et al.  A variational approach to image fusion , 2000 .

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

[19]  Masaki Suwa,et al.  Recovering high dynamic range by Multi-Exposure Retinex , 2009, J. Vis. Commun. Image Represent..

[20]  William L. Briggs,et al.  A multigrid tutorial, Second Edition , 2000 .

[21]  Erik Reinhard,et al.  Photographic tone reproduction for digital images , 2002, ACM Trans. Graph..

[22]  Jian Guan,et al.  Display HDR Image using a Gain Map , 2007, 2007 IEEE International Conference on Image Processing.

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

[24]  Jian Zhang,et al.  Adaptive local contrast enhancement for the visualization of high dynamic range images , 2008, 2008 19th International Conference on Pattern Recognition.

[25]  Heung-Yeung Shum,et al.  Radiometric calibration from a single image , 2004, CVPR 2004.