A New Image Denoising Scheme Using Spiking Neuromorphic Systems

June Oh, Jeonghwan Gwak, Aasim Rafique, and Moongu Jeon


Restricted Boltzmann Machine, Neuromorphic Systems, Image Denoising, Neural Sampling, Spiking Neural Network, Spike Time Dependent Plasticity (STDP)


It has been shown that restricted Boltzmann machines (RBMs) perform efficiently in a variety of applications such as dimensionality reduction, learning and classification. In image processing and computer vision research, image denoising has been used as a preprocessing step to estimate an original image from a noise-contaminated image by suppressing noise. In this work, we propose a new image denoising scheme based on neuromorphic platforms. Image denoising on neuromorphic hardware platforms can have significant advantages from the perspectives of scalability, concurrency and low-power consumption and real-time interaction with the given environment. From the experimental study for the MNIST hand-written digit dataset, we demonstrated that the proposed approach can generally outperform median, Gaussian and Wiener, filtering based denoising schemes, especially for a (relatively) high noise.

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