Image Super-Resolution Based on Residual Block Dense Connection

2021 ◽  
pp. 337-348
Author(s):  
Juan Chen ◽  
Ang Gao ◽  
Siqi Liu ◽  
Haiyang Jia ◽  
Yifan Shao ◽  
...  
Keyword(s):  
Super Resolution ◽  
Image Super Resolution ◽  
Residual Block

scholarly journals An Efficient Super-Resolution Network Based on Aggregated Residual Transformations

Electronics ◽  
2019 ◽  
Vol 8 (3) ◽  
pp. 339
Author(s):  
Yan Liu ◽  
Guangrui Zhang ◽  
Hai Wang ◽  
Wei Zhao ◽  
Min Zhang ◽  
...  
Keyword(s):  
Time Complexity ◽  
High Performance ◽  
Super Resolution ◽  
Experimental Results ◽  
Single Image ◽  
Residual Network ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Residual Block

In this paper, we propose an efficient multibranch residual network for single image super-resolution. Based on the idea of aggregated transformations, the split-transform-merge strategy is exploited to implement the multibranch architecture in an easy, extensible way. By this means, both the number of parameters and the time complexity are significantly reduced. In addition, to ensure the high-performance of super-resolution reconstruction, the residual block is modified and simplified with reference to the enhanced deep super-resolution network (EDSR) model. Moreover, our developed method possesses advantages of flexibility and extendibility, which are helpful to establish a specific network according to practical demands. Experimental results on both the Diverse 2K (DIV2K) and other standard datasets show that the proposed method can achieve a good performance in comparison with EDSR under the same number of convolution layers.

scholarly journals Cascading and Residual Connected Network for Single Image Superresolution

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Kai Huang ◽  
Wenhao Wang ◽  
Cheng Pang ◽  
Rushi Lan ◽  
Ji Li ◽  
...  
Keyword(s):  
Feature Learning ◽  
Super Resolution ◽  
Poor Performance ◽  
Single Image ◽  
Benchmark Datasets ◽  
Image Super Resolution ◽  
Model Training ◽  
Single Image Super Resolution ◽  
Residual Block ◽  
Quantitative Indicators

Convolution neural networks facilitate the significant process of single image super-resolution (SISR). However, most of the existing CNN-based models suffer from numerous parameters and excessively deeper structures. Moreover, these models relying on in-depth features commonly ignore the hints of low-level features, resulting in poor performance. This paper demonstrates an intriguing network for SISR with cascading and residual connections (CASR), which alleviates these problems by extracting features in a small net called head module via the strategies based on the depthwise separable convolution and deformable convolution. Moreover, we also include a cascading residual block (CAS-Block) for the upsampling process, which benefits the gradient propagation and feature learning while easing the model training. Extensive experiments conducted on four benchmark datasets demonstrate that the proposed method is superior to the latest SISR methods in terms of quantitative indicators and realistic visual effects.

A Short Survey of Image Super Resolution Algorithms

2015 ◽  
Vol 2 (2) ◽  
pp. 19-29 ◽  
Author(s):  
Heng Liu ◽  
◽  
Dongdong Huang
Keyword(s):  
Super Resolution ◽  
Short Survey ◽  
Image Super Resolution

Facial Image Super-Resolution Reconstruction Based on Separated Frequency Components

2013 ◽  
Vol E96.A (6) ◽  
pp. 1315-1322 ◽  
Author(s):  
Hyunduk KIM ◽  
Sang-Heon LEE ◽  
Myoung-Kyu SOHN ◽  
Dong-Ju KIM ◽  
Byungmin KIM
Keyword(s):  
Super Resolution ◽  
Facial Image ◽  
Frequency Components ◽  
Image Super Resolution

A Novel Approach for Image Super Resolution Problems with Convolution Neural Networks

2017 ◽  
Vol 6 (4) ◽  
pp. 15
Author(s):  
JANARDHAN CHIDADALA ◽  
RAMANAIAH K.V. ◽  
BABULU K ◽  
◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Super Resolution ◽  
Convolution Neural Networks ◽  
Novel Approach ◽  
Image Super Resolution

A Fast Decoupling Algorithm for Image Super-resolution Reconstruction of Space-invariant System

2010 ◽  
Vol 36 (2) ◽  
pp. 229-236 ◽  
Author(s):  
Li-Li HUANG ◽  
Liang XIAO ◽  
Zhi-Hui WEI ◽  
Jun ZHANG
Keyword(s):  
Super Resolution ◽  
Invariant System ◽  
Image Super Resolution ◽  
Decoupling Algorithm
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