An Information-Theoretic Approach to Multi-Exposure Fusion via Statistical Filtering using Local Entropy

J. Herwig and J. Pauli (Germany)


Image Sequence Processing, Image Synthesis, Statistical Signal Processing, Adaptive Filtering


An adaptive parameter-free image fusion method for multiple exposures of a static scene that has been captured by a stationary camera is described. The notion of a statistical convolution operator is discussed and convolution by entropy is introduced. Images are fused by weighting pixels with the amount of ambient information present in their local surroundings. The proposed fusion approach is solely based on non-structural histogram statistics. Its purely information-theoretic view contrasts the phyiscally based photometric calibration method utilized in high dynamic range (HDR) imaging.

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