Image Completion using Deep Convolutional Generative Adversarial Networks
Priyadharshini C1, S.Usha Kiruthika2, Karan Poddar3, Karthikeyan V4, Balaji Babu5
1Priyadharshini C*, Department of Information Technology, Easwari Engineering College, Ramapuram Campus, Chennai, (Tamil Nadu), India.
2Dr.S. Usha Kiruthika, SRM Institute of Science and Technology, Kattankulathur Campus, Chennai, (Tamil Nadu), India.
3Karan Poddar, Department of Information Technology, Easwari Engineering College, Ramapuram Campus, Chennai, (Tamil Nadu), India.
4Karthikeyan V, Department of Information Technology, Easwari Engineering College, Ramapuram Campus, Chennai, (Tamil Nadu), India.
5Balaji Babu, Department of Information Technology, Easwari Engineering College, Ramapuram Campus, Chennai, (Tamil Nadu), India.
Manuscript received on November 15, 2019. | Revised Manuscript received on November 23, 2019. | Manuscript published on November 30, 2019. | PP: 593-598 | Volume-8 Issue-4, November 2019. | Retrieval Number: D7725118419/2019©BEIESP | DOI: 10.35940/ijrte.D7725.118419
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© 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: Deep learning recently became the state-of-the-art in many pattern recognition tasks. Advance-ment of computational power and big datasets brings opportunity to use deep learning methods for image processing. We have used deep convolutional generative adversarial networks (DCGAN) to do various image processing tasks such as deconvolution , denoising and super-resolution. With DCGAN we can use a single architecture to perform different image processing tasks . While the results sometimes shows slightly lower PSNR for DCGAN compared to traditional methods but it tries to achieve competitive psnr scores. Thus , it allows to view quite appealing then other methods While it can learn from big data-sets very efficiently and allows itself to add high-frequency details automatically which traditional methods can’t. The architectgure in DCGAN is based on two neural networks of generator and discriminator which both tries to deceive each other and allows it to generate more appealing and realistic images from the datasets.
Keywords: (DCGAN), PSNR, While.
Scope of the Article: Deep Learning.