Abstract
Abstract To improve the quality of multi-focus image fusion in photography applications, a multi-focus image fusion algorithm based on supervised learning for fully convolutional network is proposed. The aim of this algorithm is to make the neural network learn the complementary relationship between different focusing areas of source images, which is to select different focusing positions of the source images to synthesize a global clear image. In this algorithm, focusing images are constructed as training data. Dense connection and 1 × 1 convolution are used in the network to improve the understanding ability and efficiency of the network. The result of experiment shows that the proposed algorithm is superior to other contrast algorithms in both subjective visual evaluation and objective evaluation, and the quality of image fusion is significantly improved. Code is available at https://github.com/littlebaba/SF_MFIF.
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