A Multi-Exposure Image Fusion Based on the Adaptive Weights Reflecting the Relative Pixel Intensity and Global Gradient

This paper presents a new multi-exposure fusion algorithm. The conventional approach is to define a weight map for each of the multi-exposure images, and then obtain the fusion image as their weighted sum. Most of existing methods focused on finding weight functions that assign larger weights to the pixels in better-exposed regions. While the conventional methods apply the same function to each of the multi-exposure images, we propose a function that considers all the multi-exposure images simultaneously to reflect the relative intensity between the images and global gradients. Specifically, we define two kinds of weight functions for this. The first is to measure the importance of a pixel value relative to the overall brightness and neighboring exposure images. The second is to reflect the importance of a pixel value when it is in a range with relatively large global gradient compared to other exposures. The proposed method needs modest computational complexity owing to the simple weight functions, and yet it achieves visually pleasing results and gets high scores according to an image quality measure.