Abstract
Magnetic resonance images are accompanied by random Rician noise due to the influence of uncertain factors in the process of imaging, storage, which brings a lot of inconvenience to the subsequent processing of the image and clinical diagnosis. This paper proposes an improved multipath matching pursuit algorithm based on learning Gabor pattern dictionary atom for image reconstruction and denoising. Firstly, Gabor wavelet transform based on neurophysiological constraints is used to generate dictionary atoms that match the local features of the image; Then this paper introduces adaptive differential evolution algorithm optimization to the process of solving multiple candidate atoms matching the local image features in each iteration of the multipath matching pursuit. It combines the advantages of adaptive differential evolution and multipath matching pursuit algorithm, not only avoids the genetic falling into the local optimal defect, but also obtains the best matching parameters with higher accuracy, and effectively reduces the computational complexity of the multipath matching pursuit. In the reconstruction experiment of the simulated MR images, compared with state-of-the-art denoising algorithms, our algorithm not only shows better denoising performance, but also retains more detailed information, and the running time is reduced nearly 50% than multipath matching pursuit; which fully shows the clinical application value.
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Acknowledgments
This work was supported by the National Natural Science Foundation of China (Grant No. 11471004), the National Natural Science Foundation of Shaanxi Province (Grant No. 2022ZJ-39), the Open Project of the Key Laboratory of Forensic Genetics, Ministry of Public Security (Grant No. 2021FGKFKT07).
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Li, C., Luo, Y., Yang, J., Fan, H. (2022). MR Image Denoising Based on Improved Multipath Matching Pursuit Algorithm. In: Yu, S., et al. Pattern Recognition and Computer Vision. PRCV 2022. Lecture Notes in Computer Science, vol 13535. Springer, Cham. https://doi.org/10.1007/978-3-031-18910-4_19
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DOI: https://doi.org/10.1007/978-3-031-18910-4_19
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