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A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images

A. Valli Bhasha (ECE Department, JNTUA, Ananthapuramu, India)
B.D. Venkatramana Reddy (ECE Department, JNTUA, Ananthapuramu, India)

Journal of Engineering, Design and Technology

ISSN: 1726-0531

Article publication date: 6 August 2021

Issue publication date: 6 December 2022

41

Abstract

Purpose

The problems of Super resolution are broadly discussed in diverse fields. Rather than the progression toward the super resolution models for real-time images, operating hyperspectral images still remains a challenging problem.

Design/methodology/approach

This paper aims to develop the enhanced image super-resolution model using “optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT), and Optimized Deep Convolutional Neural Network”. Once after converting the HR images into LR images, the NSSR images are generated by the optimized NSSR. Then the ADWT is used for generating the subbands of both NSSR and HRSB images. The residual image with this information is obtained by the optimized Deep CNN. All the improvements on the algorithms are done by the Opposition-based Barnacles Mating Optimization (O-BMO), with the objective of attaining the multi-objective function concerning the “Peak Signal-to-Noise Ratio (PSNR), and Structural similarity (SSIM) index”. Extensive analysis on benchmark hyperspectral image datasets shows that the proposed model achieves superior performance over typical other existing super-resolution models.

Findings

From the analysis, the overall analysis of the suggested and the conventional super resolution models relies that the PSNR of the improved O-BMO-(NSSR+DWT+CNN) was 38.8% better than bicubic, 11% better than NSSR, 16.7% better than DWT+CNN, 1.3% better than NSSR+DWT+CNN, and 0.5% better than NSSR+FF-SHO-(DWT+CNN). Hence, it has been confirmed that the developed O-BMO-(NSSR+DWT+CNN) is performing well in converting LR images to HR images.

Originality/value

This paper adopts a latest optimization algorithm called O-BMO with optimized Non-negative Structured Sparse Representation (NSSR), Adaptive Discrete Wavelet Transform (ADWT) and Optimized Deep Convolutional Neural Network for developing the enhanced image super-resolution model. This is the first work that uses O-BMO-based Deep CNN for image super-resolution model enhancement.

Keywords

Citation

Bhasha, A.V. and Reddy, B.D.V. (2022), "A multi-objective opposition-based barnacles mating optimization for image super resolution using hyper-Spectral images", Journal of Engineering, Design and Technology, Vol. 20 No. 6, pp. 1538-1564. https://doi.org/10.1108/JEDT-01-2021-0030

Publisher

:

Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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