BI-DIRECTIONAL RECURRENT NEURAL NETWORK FOR IMPROVING MULTISPECTRAL IMAGE DENOISING

Ankush Rai; Jagadeesh Kannan R

doi:10.22159/ajpcr.2017.v10s1.19678

Authors

Ankush Rai School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India
Jagadeesh Kannan R School of Computing Science & Engineering, VIT University, Chennai, Tamil Nadu, India

DOI:

https://doi.org/10.22159/ajpcr.2017.v10s1.19678

Keywords:

Recurrent Neural Network, Multispectral Imaging, Denoising Algorithm

Abstract

While procuring images form satellite the multispectral images (MSI) are often prone to noises. finding a good mathematical description of the learning based denoising model is a difficult research question and many different research accounted in the literature. Many have attempted its use with the application of neural network as a sparse learned dictionary of noisy patches. Also, this approach allows several algorithm to optimize itself for the given task at hand by using machine learning algorithm. In this study we present an improved method for learning based denoising of MSI images. Recurrent neural network used in this study helps in speeding up the computational operability and denoising performance by over 85% to 95%. Â Â Â

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