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INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT International Peer Reviewed & Refereed Journals, Open Access Journal ISSN Approved Journal No: 2456-4184 | Impact factor: 8.76 | ESTD Year: 2016 Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.76 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI) |
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| Paper Title: | Super Image Resolution |
| Authors Name: | Rituraj Mishra , Neha Chauhan , Preeti Dwivedi |
| Download E-Certificate: | Download |
| Author Reg. ID: |
IJNRD_197137
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| Published Paper Id: | IJNRD2305765 |
| Published In: | Volume 8 Issue 5, May-2023 |
| DOI: | |
| Abstract: | Deep Literacy necessitates a significant amount of info. This phrase has gained popularity among those who are considering applying deep literacy methods to their data. Enterprises regularly make significant decisions based on the prevalent idea that deep literacy only works with vast amounts of data when they do not have "big" enough data. This is not correct. Although huge amounts of data are required in some circumstances, some networks may be trained on a single image. Furthermore, without big datasets, the topology of the network itself may prevent deep networks from over-fitting in practise. We propose a deep literacy system for single-image super-resolution (SR) in this design. Our algorithm learns an end-to-end mapping between low/high quality pictures immediately. The mapping is represented by a deep convolutional neural network (CNN) that accepts the low-resolution picture as input and labours to produce the high-resolution image. We also demonstrate that classic meager-coding-based SR methods may be termed deep convolutional networks. However, unlike standard styles that manage each element individually, our method optimises all levels together. Our deep CNN has a featherlight construction yet achieves state-of-the-art restoration quality and quick speed for practical online operation. We experiment with various network architectures and parameter settings to find the best balance of performance and speed. In addition, we expand our network to handle three colour channels concurrently and demonstrate improved overall reconstruction quality. |
| Keywords: | CNN, SSIM, PSNR, RFDN, Autoencoders, Image Resolution |
| Cite Article: | "Super Image Resolution", International Journal of Novel Research and Development (www.ijnrd.org), ISSN:2456-4184, Vol.8, Issue 5, page no.h479-h483, May-2023, Available :http://www.ijnrd.org/papers/IJNRD2305765.pdf |
| Downloads: | 000118748 |
| ISSN: |
2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.76 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
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Published Paper ID:IJNRD2305765 Registration ID: 197137 Published In: Volume 8 Issue 5, May-2023 DOI (Digital Object Identifier): Page No: h479-h483 Country: Lucknow, UTTAR PRADESH, India Research Area: Health Science Publisher : IJ Publication Published Paper URL : https://www.ijnrd.org/viewpaperforall?paper=IJNRD2305765 Published Paper PDF: https://www.ijnrd.org/papers/IJNRD2305765 |
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ISSN: 2456-4184
Impact Factor: 8.76 and ISSN APPROVED
Journal Starting Year (ESTD) : 2016
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