Image Completion using Spiking Neural Networks
Vineet Kumar1, A. K. Sinha2, A. K. Solanki3
1Vineet Kumar, Computer Science & Engineering, Noida Institute of Engineering & Technology, Greater Noida, India.
2Dr. A. K. Sinha, Director, UST Software India Pvt Ltd., New Delhi, India
3Dr. A. K. Solanki, Department of Computer Science & Engineering, BIET, Jhansi, India.
Manuscript received on October 12, 2019. | Revised Manuscript received on 22 October, 2019. | Manuscript published on November 10, 2019. | PP: 4449-4452 | Volume-9 Issue-1, November 2019. | Retrieval Number: A5294119119/2019©BEIESP | DOI: 10.35940/ijitee.A5294.119119
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper, we are showing how spiking neural networks are applied in image repainting, and its results are outstanding compared with other machine learning techniques. Spiking Neural Networks uses the shape of patterns and shifting distortion on images and positions to retrieve the original picture. Thus, Spiking Neural Networks is one of the advanced generations and third generation of machine learning techniques, and is an extension to the concept of Neural Networks and Convolutional Neural Networks. Spiking Neural Networks (SNN) is biologically plausible, computationally more powerful, and is considerably faster. The proposed algorithm is tested on different sized digital images over which free form masks are applied. The performance of the algorithm is examined to find the PSNR, QF and SSIM. The model has an effective and fast to complete the image by filling the gaps (holes).
Keywords: Biological Neuron Model, Image Inpainting, Machine Learning, response entropy, spiking neural networks, STDP rule.
Scope of the Article: Machine Learning