Abstract:
MEF-Net is a fast multi-exposure image fusion (MEF) method for static image sequences of any spatial resolution and exposure number.
A fully convolutional network predicts weight maps from a low-resolution input sequence. Guided filters upsample the weight maps. Weighted fusion generates the image.
MEF-Net optimizes the perceptually calibrated MEF structural similarity (MEF-SSIM) index over a database of training sequences at full resolution.
The optimized MEF-Net improves visual quality for most sequences and runs 10 to 1000 times faster than state-of-the-art methods across an independent set of test sequences.
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