scholarly journals Image Super-resolution Method Using Registration of Multi-scale Wavelet Components with Consideration of Digital Cinema Noise

2014 ◽  
Vol 68 (2) ◽  
pp. J92-J98 ◽  
Author(s):  
Yasutaka Matsuo ◽  
Ryoki Takada ◽  
Shinya Iwasaki ◽  
Jiro Katto
Keyword(s):  
Super Resolution ◽  
Resolution Method ◽  
Digital Cinema ◽  
Multi Scale ◽  
Image Super Resolution

scholarly journals Enhanced Single Image Super Resolution Method Using Lightweight Multi-Scale Channel Dense Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3351
Author(s):  
Yooho Lee ◽  
Dongsan Jun ◽  
Byung-Gyu Kim ◽  
Hunjoo Lee
Keyword(s):  
Super Resolution ◽  
Perceptual Quality ◽  
Dense Network ◽  
Single Image ◽  
Resolution Method ◽  
Multi Scale ◽  
Training Images ◽  
Image Super Resolution ◽  
Single Image Super Resolution ◽  
Sr Method

Super resolution (SR) enables to generate a high-resolution (HR) image from one or more low-resolution (LR) images. Since a variety of CNN models have been recently studied in the areas of computer vision, these approaches have been combined with SR in order to provide higher image restoration. In this paper, we propose a lightweight CNN-based SR method, named multi-scale channel dense network (MCDN). In order to design the proposed network, we extracted the training images from the DIVerse 2K (DIV2K) dataset and investigated the trade-off between the SR accuracy and the network complexity. The experimental results show that the proposed method can significantly reduce the network complexity, such as the number of network parameters and total memory capacity, while maintaining slightly better or similar perceptual quality compared to the previous methods.

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-sensor image super-resolution with fuzzy cluster by using multi-scale and multi-view sparse coding for infrared image

2017 ◽  
Vol 76 (23) ◽  
pp. 24871-24902 ◽  
Author(s):  
Xiaomin Yang ◽  
Wei Wu ◽  
Kai Liu ◽  
Weilong Chen ◽  
Ping Zhang ◽  
...  
Keyword(s):  
Sparse Coding ◽  
Infrared Image ◽  
Super Resolution ◽  
Fuzzy Cluster ◽  
Multi Scale ◽  
Image Super Resolution

Mural Image Super Resolution Reconstruction Based on Multi-Scale Residual Attention Network

2020 ◽  
Vol 57 (16) ◽  
pp. 161012
Author(s):  
徐志刚 Xu Zhigang ◽  
闫娟娟 Yan Juanjuan ◽  
朱红蕾 Zhu Honglei
Keyword(s):  
Super Resolution ◽  
Attention Network ◽  
Multi Scale ◽  
Image Super Resolution
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