A new method for super-resolution reconstruction using wavelet transform

2005 ◽  
Author(s):  
Shengwei Pei ◽  
Haiyan Feng ◽  
Minghui Du
Keyword(s):  
Wavelet Transform ◽  
Super Resolution ◽  
New Method

scholarly journals A New Method for Fundamental Signal Extraction Based on Wavelet Transform

Automatika ◽  
2016 ◽  
Vol 57 (2) ◽  
Author(s):  
Javad Modarresi ◽  
Eskandar Gholipour
Keyword(s):  
Wavelet Transform ◽  
New Method ◽  
Signal Extraction

Block unshifting high-accuracy motion estimation: A new method adapted to super-resolution enhancement

2018 ◽  
Vol 65 ◽  
pp. 81-93
Author(s):  
Konstantinos Konstantoudakis ◽  
Lazaros Vrysis ◽  
Nikolaos Tsipas ◽  
Charalampos Dimoulas
Keyword(s):  
Motion Estimation ◽  
Resolution Enhancement ◽  
Super Resolution ◽  
High Accuracy ◽  
New Method

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

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Valli Bhasha A. ◽  
Venkatramana Reddy B.D.
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Discrete Wavelet ◽  
Content Type ◽  
Resolution Model ◽  
Deep Cnn ◽  
Image Super Resolution ◽  
Better Than

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.

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