Abstract
Image restoration aimed to recover the original image to from degraded images and degenerate function. Fuzzy logic systems and neural network can complement each other quite well. In this paper, a novel Image Restoration approach is developed. Wavelet Semi-soft Threshold Transform and of our method is utilized to image restoration. Firstly, Wavelet Semi-soft Threshold Transform method is used to image denoising. Then, the image is classified into several regions using fuzzy sets, which are smoothing, texture and edge regions to obtain the input of BP Fuzzy Neural Network. Sliding window is used to extract features and input the training data. Finally, the output of BP Fuzzy Neural Network is the restored image.
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Acknowledgments
This work was supported by the NSFC (61327807, 6152 1091, 61520106010, 61134005), and the National Basic Research Program of China (973 Program: 2012CB821200, 2012CB821201).
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Pei, W., Jia, Y. (2020). Image Restoration Based on Wavelet Semi-soft Threshold Transform and BP Fuzzy Neural Network. In: Deng, Z. (eds) Proceedings of 2019 Chinese Intelligent Automation Conference. CIAC 2019. Lecture Notes in Electrical Engineering, vol 586. Springer, Singapore. https://doi.org/10.1007/978-981-32-9050-1_70
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DOI: https://doi.org/10.1007/978-981-32-9050-1_70
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Online ISBN: 978-981-32-9050-1
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