scholarly journals Wavelet Domain Multidictionary Learning for Single Image Super-Resolution

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xiaomin Wu ◽  
Jiulun Fan ◽  
Jian Xu ◽  
Yanzi Wang
Keyword(s):  
Wavelet Transformation ◽  
Super Resolution ◽  
Principal Component ◽  
Discrete Wavelet ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Relationship Of ◽  
The Given ◽  
The Relationship

Image super-resolution (SR) aims at recovering the high-frequency (HF) details of a high-resolution (HR) image according to the given low-resolution (LR) image and some priors about natural images. Learning the relationship of the LR image and its corresponding HF details to guide the reconstruction of the HR image is needed. In order to alleviate the uncertainty in HF detail prediction, the HR and LR images are usually decomposed into 4 subbands after 1-level discrete wavelet transformation (DWT), including an approximation subband and three detail subbands. From our observation, we found the approximation subbands of the HR image and the corresponding bicubic interpolated image are very similar but the respective detail subbands are different. Therefore, an algorithm to learn 4 coupled principal component analysis (PCA) dictionaries to describe the relationship between the approximation subband and the detail subbands is proposed in this paper. Comparisons with various state-of-the-art methods by experiments showed that our proposed algorithm is superior to some related works.

scholarly journals Wavelet-Integrated Deep Networks for Single Image Super-Resolution

Electronics ◽  
2019 ◽  
Vol 8 (5) ◽  
pp. 553 ◽  
Author(s):  
Faisal Sahito ◽  
Pan Zhiwen ◽  
Junaid Ahmed ◽  
Raheel Ahmed Memon
Keyword(s):  
Wavelet Transform ◽  
Similarity Index ◽  
Super Resolution ◽  
Training Data ◽  
Discrete Wavelet ◽  
Single Image ◽  
Data Set ◽  
Proposed Model ◽  
Image Super Resolution ◽  
Single Image Super Resolution

We propose a scale-invariant deep neural network model based on wavelets for single image super-resolution (SISR). The wavelet approximation images and their corresponding wavelet sub-bands across all predefined scale factors are combined to form a big training data set. Then, mappings are determined between the wavelet sub-band images and their corresponding approximation images. Finally, the gradient clipping process is used to boost the training speed of the algorithm. Furthermore, stationary wavelet transform (SWT) is used instead of a discrete wavelet transform (DWT), due to its up-scaling property. In this way, we can preserve more information about the images. In the proposed model, the high-resolution image is recovered with detailed features, due to redundancy (across the scale) property of wavelets. Experimental results show that the proposed model outperforms state-of-the algorithms in terms of peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM).

scholarly journals Real-Time Environment Monitoring Using a Lightweight Image Super-Resolution Network

2021 ◽  
Vol 18 (11) ◽  
pp. 5890
Author(s):  
Qiang Yu ◽  
Feiqiang Liu ◽  
Long Xiao ◽  
Zitao Liu ◽  
Xiaomin Yang
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Super Resolution ◽  
Model Complexity ◽  
Practical Application ◽  
Single Image ◽  
Feature Maps ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Deep-learning (DL)-based methods are of growing importance in the field of single image super-resolution (SISR). The practical application of these DL-based models is a remaining problem due to the requirement of heavy computation and huge storage resources. The powerful feature maps of hidden layers in convolutional neural networks (CNN) help the model learn useful information. However, there exists redundancy among feature maps, which can be further exploited. To address these issues, this paper proposes a lightweight efficient feature generating network (EFGN) for SISR by constructing the efficient feature generating block (EFGB). Specifically, the EFGB can conduct plain operations on the original features to produce more feature maps with parameters slightly increasing. With the help of these extra feature maps, the network can extract more useful information from low resolution (LR) images to reconstruct the desired high resolution (HR) images. Experiments conducted on the benchmark datasets demonstrate that the proposed EFGN can outperform other deep-learning based methods in most cases and possess relatively lower model complexity. Additionally, the running time measurement indicates the feasibility of real-time monitoring.

Compact and progressive network for enhanced single image super-resolution—ComPrESRNet

2021 ◽  
Author(s):  
Vishal Chudasama ◽  
Kishor Upla ◽  
Kiran Raja ◽  
Raghavendra Ramachandra ◽  
Christoph Busch
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

scholarly journals Noisy Single Image Super-resolution Based on Local Fractal Feature Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Kai Shao ◽  
Qinglan Fan ◽  
Yunfeng Zhang ◽  
Fangxun Bao ◽  
Caiming Zhang
Keyword(s):  
Super Resolution ◽  
Feature Analysis ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution
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