A New Optimized Recurrent Feedback Deep Convolutional Neural Net for Image Super Resolution
Janhavi H. Borse1, Dipti D. Patil2
1Janhavi H. Borse, Department of Computer Engineering, Sandip Institute of Technology & Research Centre, Nashik, India.
2Dipti D. Patil, Department of Information Technology, MKSSS’s Cummins College of Engineering for Women, Pune, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 22 August 2019. | Manuscript published on 30 September 2019. | PP: 5958-5965 | Volume-8 Issue-3 September 2019 | Retrieval Number: C6255098319/19©BEIESP | DOI: 10.35940/ijrte.C6255.098319
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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: Now-a-days many applications dealing with visual content need to access underlying details in the image or video of interest. For instance, detailing is required to take life critical decisions for further action plans by a doctor. Clarity and structural information are some of the aspects of detailing. It can be achieved by cost effective software solution like super resolution reconstruction of an image. Super resolution (SR) deals in increasing resolution of an image to make it more clear and valid for use. Many SR techniques exist with variable goals to achieve. With this intension a new technique for preserving structural information in the reconstruction process is proposed. The system extends a deep convolution neural network by adding a new optimization layer at the end of network activation layer. This new layer maintains permissible error threshold in the acquired signal and tries to improve the signal by feeding back latest reconstructed frame. The proposed system shows noticeable improvement in structural similarity of reconstructed images as compared with the ground truth.
Keywords: Convolutional Neural Network, Optimization Layer, Reconstruction, Structural Similarity, Super Resolution
Scope of the Article: High Speed Networks