A fuzzy convolutional neural network for enhancing multi-focus image fusion |
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Affiliation: | 1. Department of Electrical Engineering and Computer Science, National Taipei University of Technology, Taipei 10608, Taiwan;2. Department of Systemics, School of Computer Science, University of Petroleum & Energy Studies, Dehradun, India;3. Chitkara University School of Engineering and Technology, Chitkara University, Himachal Pradesh, India;4. Department of Computer Science and Information Management, Providence University, Taichung 43301, Taiwan;5. Department of Computer Science, College of Computers and Information Technology, Taif University, P.O. BOX 11099, Taif 21944, Saudi Arabia |
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Abstract: | The images captured by the cameras contain distortions, misclassified pixels, uncertainties and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input image features to produce a single fused image using all its objects in focus. However, it is computationally complex, which leads to inconsistency. Hence, the MFIF method is employed to generate the fused image by integrating the fuzzy sets (FS) and convolutional neural network (CNN) to detect focused and unfocused parts in both source images. It is also compared with other competing six MFIF methods like Neutrosophic set based stationary wavelet transform (NSWT), guided filters, CNN, ensemble CNN, image fusion-based CNN and deep regression pair learning (DRPL). Benchmark datasets validate the superiority of the proposed FCNN method in terms of four non-reference assessment measures having mutual information (1.1678), edge information (0.7281), structural similarity (0.9850) and human perception (0.8020) and two reference metrics such as Peak signal-to-noise ratio (57.23) and root mean square error (1.814). |
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Keywords: | Deep learning Fusion Fuzzy sets Multi-focus images |
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