scholarly journals Accurate Spectral Super-Resolution from Single RGB Image Using Multi-scale CNN

2018 ◽  
pp. 206-217 ◽  
Author(s):  
Yiqi Yan ◽  
Lei Zhang ◽  
Jun Li ◽  
Wei Wei ◽  
Yanning Zhang
Keyword(s):  
Super Resolution ◽  
Multi Scale ◽  
Rgb Image

scholarly journals Multi-FAN: multi-spectral mosaic super-resolution via multi-scale feature aggregation network

2021 ◽  
Vol 32 (2) ◽  
Author(s):  
Mehrdad Sheoiby ◽  
Sadegh Aliakbarian ◽  
Saeed Anwar ◽  
Lars Petersson
Keyword(s):  
Super Resolution ◽  
Scale Feature ◽  
Multi Scale ◽  
Feature Aggregation

scholarly journals Single image super resolution based on multi-scale structure and non-local smoothing

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Wenyi Wang ◽  
Jun Hu ◽  
Xiaohong Liu ◽  
Jiying Zhao ◽  
Jianwen Chen
Keyword(s):  
Super Resolution ◽  
Principal Component ◽  
Feature Mapping ◽  
Multi Scale ◽  
Non Local ◽  
Image Super Resolution ◽  
Gaussian Blur ◽  
Single Image Super Resolution ◽  
Global And Local ◽  
Bicubic Interpolation

AbstractIn this paper, we propose a hybrid super-resolution method by combining global and local dictionary training in the sparse domain. In order to present and differentiate the feature mapping in different scales, a global dictionary set is trained in multiple structure scales, and a non-linear function is used to choose the appropriate dictionary to initially reconstruct the HR image. In addition, we introduce the Gaussian blur to the LR images to eliminate a widely used but inappropriate assumption that the low resolution (LR) images are generated by bicubic interpolation from high-resolution (HR) images. In order to deal with Gaussian blur, a local dictionary is generated and iteratively updated by K-means principal component analysis (K-PCA) and gradient decent (GD) to model the blur effect during the down-sampling. Compared with the state-of-the-art SR algorithms, the experimental results reveal that the proposed method can produce sharper boundaries and suppress undesired artifacts with the present of Gaussian blur. It implies that our method could be more effect in real applications and that the HR-LR mapping relation is more complicated than bicubic interpolation.

Multi-scale channel network based on filer pruning for image super-resolution

Optik ◽  
2021 ◽  
Vol 236 ◽  
pp. 166641
Author(s):  
Deyun Wei ◽  
Zhaowu Wang
Keyword(s):  
Super Resolution ◽  
Channel Network ◽  
Multi Scale ◽  
Image Super Resolution

Learning depth super-resolution by using multi-scale convolutional neural network

2019 ◽  
Vol 36 (2) ◽  
pp. 1773-1783 ◽  
Author(s):  
Masoumeh Zareapoor ◽  
Pourya Shamsolmoali ◽  
Jie Yang
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Super Resolution ◽  
Multi Scale

scholarly journals Spatio-Temporal Super-Resolution Reconstruction of Remote-Sensing Images Based on Adaptive Multi-Scale Detail Enhancement

Sensors ◽  
2018 ◽  
Vol 18 (2) ◽  
pp. 498 ◽  
Author(s):  
Hong Zhu ◽  
Xinming Tang ◽  
Junfeng Xie ◽  
Weidong Song ◽  
Fan Mo ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Super Resolution ◽  
Remote Sensing Images ◽  
Multi Scale ◽  
Spatio Temporal ◽  
Detail Enhancement
Sign in / Sign up

Export Citation Format

Share Document