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Multi-Exposure Image Fusion Based on Information-Theoretic Channel

We propose a method for multi-exposure image fusion based on information-theoretic channel. In the fusion scheme, conditional entropy, as an information measurement of each pixel in one image to the other image, is calculated through an information channel built between two source images, and then weight maps of the source images are generated. Considering the noise caused by blending source images with weight maps directly, we exploit Laplacian pyramid decomposition to avoid unpleasing artifacts. Based on this pyramid scheme, images at every scale are fused by weight maps, and a final fused image is inversely reconstructed. The proposed method is easy to implement and delivers results which are competitive with state-of-art methods.