Edge-preserving single image super-resolution

2011 ◽  
Author(s):  
Qiang Zhou ◽  
Shifeng Chen ◽  
Jianzhuang Liu ◽  
Xiaoou Tang
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Edge Preserving ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Robust Single-Image Super-Resolution Based on Adaptive Edge-Preserving Smoothing Regularization

2018 ◽  
Vol 27 (6) ◽  
pp. 2650-2663 ◽  
Author(s):  
Shuying Huang ◽  
Jun Sun ◽  
Yong Yang ◽  
Yuming Fang ◽  
Pan Lin ◽  
...  
Keyword(s):  
Super Resolution ◽  
Single Image ◽  
Edge Preserving ◽  
Image Super Resolution ◽  
Single Image Super Resolution

An Improved Iterative Back Projection Based Single Image Super Resolution Approach

2014 ◽  
Vol 14 (04) ◽  
pp. 1450015 ◽  
Author(s):  
Milan N. Bareja ◽  
Chintan K. Modi
Keyword(s):  
Signal To Noise Ratio ◽  
Super Resolution ◽  
Absolute Error ◽  
Back Projection ◽  
Single Image ◽  
Edge Preserving ◽  
Difference Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Iterative Back Projection

In this paper, an effort is made to propose an effective image super resolution (SR) approach to recover a high resolution (HR) image from a single low resolution (LR) image. This approach is based on an iterative back projection (IBP) method with the edge preserving infinite symmetric exponential filter (ISEF) and difference image. Amalgamation of ISEF and difference image provides high frequency information. This approach is applied on different type of images and compared results with different existing image SR approaches. Simulation results demonstrate that proposed approach can more precisely enlarge the LR image. This proposed approach decreases mean square error (MSE) and mean absolute error (MAE) and increases the peak signal-to-noise ratio (PSNR) significantly compared to other existing approaches.

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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