A method for remote sensing image restoration using gyroscope sensor

2017 ◽  
Author(s):  
Huajun Feng ◽  
Lirong He ◽  
Zhihai Xu ◽  
Qi Li ◽  
Yueting Chen ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Remote Sensing Image ◽  
Gyroscope Sensor

Stacked lossless deconvolutional network for remote sensing image restoration

2020 ◽  
Vol 14 (01) ◽  
pp. 1
Author(s):  
Changyeop Shin ◽  
Minbeom Kim ◽  
Sungho Kim ◽  
Youngjung Kim
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Remote Sensing Image

HS remote sensing image restoration using fusion with MS images by EM algorithm

2017 ◽  
Vol 11 (1) ◽  
pp. 95-103 ◽  
Author(s):  
Amir Ansari ◽  
Habibollah Danyali ◽  
Mohammad Sadegh Helfroush
Keyword(s):  
Remote Sensing ◽  
Em Algorithm ◽  
Image Restoration ◽  
Remote Sensing Image

Adaptive dual-tree complex wavelet algorithm for remote sensing image restoration

2014 ◽  
Vol 26 (10) ◽  
pp. 101003
Author(s):  
文奴 Wen Nu ◽  
杨世植 Yang Shizhi ◽  
崔生成 Cui Shengcheng ◽  
程伟 Cheng Wei
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Remote Sensing Image ◽  
Complex Wavelet ◽  
Wavelet Algorithm

Image restoration for large-motion blurred lunar remote sensing image

2017 ◽  
Author(s):  
Zhihai Xu ◽  
Pengzhao Ye ◽  
Guangmang Cui ◽  
Huajun Feng ◽  
Qi Li ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Image Restoration ◽  
Remote Sensing Image ◽  
Large Motion ◽  
Lunar Remote Sensing

scholarly journals Deep Memory Connected Neural Network for Optical Remote Sensing Image Restoration

2018 ◽  
Vol 10 (12) ◽  
pp. 1893 ◽  
Author(s):  
Wenjia Xu ◽  
Guangluan Xu ◽  
Yang Wang ◽  
Xian Sun ◽  
Daoyu Lin ◽  
...  
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Image Restoration ◽  
Spatial Resolution ◽  
Remote Sensing Image ◽  
Global Memory ◽  
Great Success ◽  
Optical Remote Sensing ◽  
Remote Sensing Images ◽  
Feature Maps

The spatial resolution and clarity of remote sensing images are crucial for many applications such as target detection and image classification. In the last several decades, tremendous image restoration tasks have shown great success in ordinary images. However, since remote sensing images are more complex and more blurry than ordinary images, most of the existing methods are not good enough for remote sensing image restoration. To address such problem, we propose a novel method named deep memory connected network (DMCN) based on the convolutional neural network to reconstruct high-quality images. We build local and global memory connections to combine image detail with global information. To further reduce parameters and ease time consumption, we propose Downsampling Units, shrinking the spatial size of feature maps. We verify its capability on two representative applications, Gaussian image denoising and single image super-resolution (SR). DMCN is tested on three remote sensing datasets with various spatial resolution. Experimental results indicate that our method yields promising improvements and better visual performance over the current state-of-the-art. The PSNR and SSIM improvements over the second best method are up to 0.3 dB.

Sign in / Sign up

Export Citation Format

Share Document