Multispectral Image Super-Resolution Using Structure-Guided RGB Image Fusion

2018 ◽  
pp. 155-167
Author(s):  
Zhi-Wei Pan ◽  
Hui-Liang Shen
Keyword(s):  
Image Fusion ◽  
Super Resolution ◽  
Multispectral Image ◽  
Image Super Resolution ◽  
Rgb Image

Remote Sensing Image Fusion Based on Multispectral Image Super-Resolution

2016 ◽  
Vol 53 (2) ◽  
pp. 021001
Author(s):  
杨超 Yang Chao ◽  
杨斌 Yang Bin ◽  
黄国玉 Huang Guoyu
Keyword(s):  
Remote Sensing ◽  
Image Fusion ◽  
Super Resolution ◽  
Remote Sensing Image ◽  
Multispectral Image ◽  
Remote Sensing Image Fusion ◽  
Image Super Resolution

scholarly journals Hyperspectral Image Super-Resolution With a Mosaic RGB Image

2018 ◽  
Vol 27 (11) ◽  
pp. 5539-5552 ◽  
Author(s):  
Ying Fu ◽  
Yinqiang Zheng ◽  
Hua Huang ◽  
Imari Sato ◽  
Yoichi Sato
Keyword(s):  
Hyperspectral Image ◽  
Super Resolution ◽  
Image Super Resolution ◽  
Rgb Image

Video super resolution using convolutional neural network and image fusion techniques

2021 ◽  
Vol 24 (4) ◽  
pp. 279-287
Author(s):  
Vikas Kumar ◽  
Tanupriya Choudhury ◽  
Suresh Chandra Satapathy ◽  
Ravi Tomar ◽  
Archit Aggarwal
Keyword(s):  
Neural Network ◽  
High Resolution ◽  
Convolutional Neural Network ◽  
Image Fusion ◽  
Reference Model ◽  
Super Resolution ◽  
Motion Blur ◽  
Single Image ◽  
Image Super Resolution ◽  
Single Image Super Resolution

Recently, huge progress has been achieved in the field of single image super resolution which augments the resolution of images. The idea behind super resolution is to convert low-resolution images into high-resolution images. SRCNN (Single Resolution Convolutional Neural Network) was a huge improvement over the existing methods of single-image super resolution. However, video super-resolution, despite being an active field of research, is yet to benefit from deep learning. Using still images and videos downloaded from various sources, we explore the possibility of using SRCNN along with image fusion techniques (minima, maxima, average, PCA, DWT) to improve over existing video super resolution methods. Video Super-Resolution has inherent difficulties such as unexpected motion, blur and noise. We propose Video Super Resolution – Image Fusion (VSR-IF) architecture which utilizes information from multiple frames to produce a single high- resolution frame for a video. We use SRCNN as a reference model to obtain high resolution adjacent frames and use a concatenation layer to group those frames into a single frame. Since, our method is data-driven and requires only minimal initial training, it is faster than other video super resolution methods. After testing our program, we find that our technique shows a significant improvement over SCRNN and other single image and frame super resolution techniques.

Multispectral image fusion using super-resolution conditional generative adversarial networks

2018 ◽  
Vol 13 (02) ◽  
pp. 1 ◽  
Author(s):  
Junhao Zhang ◽  
Pourya Shamsolmoali ◽  
Pengpeng Zhang ◽  
Deying Feng ◽  
Jie Yang
Keyword(s):  
Image Fusion ◽  
Super Resolution ◽  
Multispectral Image ◽  
Generative Adversarial Networks ◽  
Adversarial Networks
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