ISSN 2394-5125
 

Research Article 


Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN)

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela.

Abstract
A selfie is typically a self-portrait captured using the front camera of a smart phone. Most stateof-the-art smart phones are fortified using a high-resolution (HR) hind camera and a low-resolution (LR) obverse camera. Since selfies are bagged by obverse camera with restricted pixel resolution, the fine details in it are explicitly missed. This paper intends a fast super-resolution (SR) algorithm using convolution neural network (CNN). Dataset used in this work is selfie images. The dataset has training set of 65,700 and testing set of 7,700. Total samples used are 73,400 which is separated by 45 labels. The algorithm used in this work is established on deep learning with appropriate some layer which shows significant improvement in accuracy and reduced the error rate. The investigational consequences demonstrate that the projected process produces better HR images than the existing SR methods, while providing fast running time.

Key words: selfie, convolution neural network (CNN), low-resolution (LR), high-resolution (HR), super-resolution (SR)


 
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How to Cite this Article
Pubmed Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). JCR. 2020; 7(5): 1260-1267. doi:10.31838/jcr.07.05.234


Web Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). http://www.jcreview.com/?mno=97223 [Access: August 16, 2021]. doi:10.31838/jcr.07.05.234


AMA (American Medical Association) Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). JCR. 2020; 7(5): 1260-1267. doi:10.31838/jcr.07.05.234



Vancouver/ICMJE Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). JCR. (2020), [cited August 16, 2021]; 7(5): 1260-1267. doi:10.31838/jcr.07.05.234



Harvard Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela (2020) Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). JCR, 7 (5), 1260-1267. doi:10.31838/jcr.07.05.234



Turabian Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. 2020. Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). Journal of Critical Reviews, 7 (5), 1260-1267. doi:10.31838/jcr.07.05.234



Chicago Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. "Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN)." Journal of Critical Reviews 7 (2020), 1260-1267. doi:10.31838/jcr.07.05.234



MLA (The Modern Language Association) Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela. "Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN)." Journal of Critical Reviews 7.5 (2020), 1260-1267. Print. doi:10.31838/jcr.07.05.234



APA (American Psychological Association) Style

Dr.G.Nirmalapriya, Dr.R.Rajeswari, K. .Jayamani, S.Sheela (2020) Selfie Image Super-resolution using weighted feature Convolution Neural Network (WFCNN). Journal of Critical Reviews, 7 (5), 1260-1267. doi:10.31838/jcr.07.05.234