This is a Matlab implementation of SAR Image Super Resolution. SAR images being inherently affected by speckle noise fails on using natural image super-resolution methods. The code presented here simultaneously denoises and super-resolves to a single high-resolution frame from multiple low-resolution images.
Authors: Sithara Kanakaraj, Madhu S. Nair and Saidalavi Kalady
This code is the Matlab implementation of the paper
Sithara Kanakaraj, Madhu S. Nair and Saidalavi Kalady, “Adaptive Importance Sampling Unscented Kalman Filter based SAR Image Super Resolution”, Computers and Geosciences, Elsevier, Vol. 133, Article No. 104310, December 2019.
DOI: https://doi.org/10.1016/j.cageo.2019.104310
To run the code, please use the main function Adaptive_ISUKF.m.
The test images are present in the data folder. Each folder consists of
synthetic and real SAR images. Each input LR image consists 16 low-resolution images.
Format: - filePath: the file name of the input Example: filePath = 'data\synthetic\synthetic1.mat';
A high-resolution image magnified to a factor of 2 is presented as the output along
with the values of quality assessment metrics.
- HR: the super-resolved image
1. For Image Registration: Manuel Guizar-Sicairos, Samuel T. Thurman, and James R. Fienup,
"Efficient subpixel image registration algorithms," Opt. Lett. 33, 156-158 (2008).
2. For Noise estimation: Xinhao Liu, Masayuki Tanaka and Masatoshi Okutomi,
"Single-Image Noise Level Estimation for Blind Denoising Noisy Image", IEEE Transactions
on Image Processing, Vol.22, No.12, pp.5226-5237, December, 2013.
3. For Structural Similarity Index Metric (SSIM): Z. Wang, A. C. Bovik, H. R. Sheikh, and
E. P. Simoncelli, "Image quality assessment: From error visibility to structural similarity,"
IEEE Transactios on Image Processing, vol. 13, no. 4, pp. 600-612, Apr. 2004.
4 For Feature Similarity Index Metric (FSIM): Lin Zhang, Lei Zhang, Xuanqin Mou, and David Zhang,
"FSIM: a feature similarity index for image qualtiy assessment", IEEE Transactions on Image
Processing, vol. 20, no. 8, pp. 2378-2386, 2011.
Please cite our paper if you find the software useful for your work.
@article{kanakaraj2019adaptive,
title={Adaptive Importance Sampling Unscented Kalman Filter based SAR image super resolution},
author={Kanakaraj, Sithara and Nair, Madhu S and Kalady, Saidalavi},
journal={Computers \& Geosciences},
volume={133},
pages={104310},
year={2019},
publisher={Elsevier}
}
Disclaimer: The assessment metric values in the paper are the best results for the ideal cases. It may change at every execution.