Robust Single Image Super-Resolution via Deep Networks With Sparse Prior

2016 ◽  
Vol 25 (7) ◽  
pp. 3194-3207 ◽  
Author(s):  
Ding Liu ◽  
Zhaowen Wang ◽  
Bihan Wen ◽  
Jianchao Yang ◽  
Wei Han ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Deep Networks ◽  
Image Super Resolution ◽  
Sparse Prior ◽  
Single Image Super Resolution

Lightweight Single Image Super-resolution with Dense Connection Distillation Network

2021 ◽  
Vol 17 (1s) ◽  
pp. 1-17
Author(s):  
Yanchun Li ◽  
Jianglian Cao ◽  
Zhetao Li ◽  
Sangyoon Oh ◽  
Nobuyoshi Komuro
Keyword(s):  
Feature Fusion ◽  
Super Resolution ◽  
Single Image ◽  
Feature Maps ◽  
Deep Networks ◽  
Benchmark Datasets ◽  
Rich Information ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Training Efficiency

Single image super-resolution attempts to reconstruct a high-resolution (HR) image from its corresponding low-resolution (LR) image, which has been a research hotspot in computer vision and image processing for decades. To improve the accuracy of super-resolution images, many works adopt very deep networks to model the translation from LR to HR, resulting in memory and computation consumption. In this article, we design a lightweight dense connection distillation network by combining the feature fusion units and dense connection distillation blocks (DCDB) that include selective cascading and dense distillation components. The dense connections are used between and within the distillation block, which can provide rich information for image reconstruction by fusing shallow and deep features. In each DCDB, the dense distillation module concatenates the remaining feature maps of all previous layers to extract useful information, the selected features are then assessed by the proposed layer contrast-aware channel attention mechanism, and finally the cascade module aggregates the features. The distillation mechanism helps to reduce training parameters and improve training efficiency, and the layer contrast-aware channel attention further improves the performance of model. The quality and quantity experimental results on several benchmark datasets show the proposed method performs better tradeoff in term of accuracy and efficiency.

Clustering based multiple branches deep networks for single image super-resolution

2018 ◽  
Vol 79 (13-14) ◽  
pp. 9019-9035 ◽  
Author(s):  
Zhen Li ◽  
Qilei Li ◽  
Wei Wu ◽  
Zongjun Wu ◽  
Lu Lu ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Deep Networks ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Blind single image super-resolution with a mixture of deep networks

2020 ◽  
Vol 102 ◽  
pp. 107169 ◽  
Author(s):  
Yifan Wang ◽  
Lijun Wang ◽  
Hongyu Wang ◽  
Peihua Li ◽  
Huchuan Lu
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Deep Networks ◽  
Image Super Resolution ◽  
Single Image Super Resolution

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
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