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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM45834

Optimized Convolutional Neural Network for High Quality Image Super Resolution

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM45834

Volume:

9 |

4 |

Apr 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/ijsrem45834

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

M

M.H.Ananya

GsI

Gnaneshwara sairam Indrala

CR

Ch. Rohith

MA.

Ms. A .Harika

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Abstract

—- – One of the most crucial methods for digital image processing is super-resolution. In this method, one sub category is Single Image Super Resolution (SISR) and other is Multi-Frame Super Resolution (MFSR). A low-resolution image shall be used by SISR to output a high-resolution image whereas in MFSR various images of the same view with slight variations in positions are considered collectively as input to produce a single high- resolution image. In our paper Peak Signal Noise Ratio (PSNR), Structural Similarity Index Metric (SSIM), Multi Scale Structural Similarity Index Metric (MSSSIM) and Weighted Peak Signal Noise Ratio (WPSNR) are the objective metrics used to evaluate Very Deep Super Resolution Network (VDSR) and Super Resolution Convolutional Neural Network (SRCNN) models of image super resolution

KEYWORDS

Image Super-Resolution Convolutional Neural Network Lightweight Networks Deep Learning LESRCNN Structural Similarity Index Metric Peak Signal Noise Ratio Multi Scale Structural Similarity Index Metric and Weighted Peak Signal Noise Ratio

Cite This Article

M.H.Ananya, Gnaneshwara sairam Indrala, Ch. Rohith, Ms. A .Harika, "Optimized Convolutional Neural Network for High Quality Image Super Resolution," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 4, Apr 2025. https://doi.org/10.55041/ijsrem45834

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Publication Date

4/24/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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