Multispectral Image Denoising using Bi-Directional Recurrent Neural Network with DPCA Algorithm
In practices, a Multispectral Images (MSI) image is always prone to corruption by various sources of noises while procuring the images. In this paper we implemented Decomposable Pixel Component Analysis (DPCA) algorithm with recurrent neural network (RNN) which effectively denoised the MSI images. The RNN enables shrinkage of non-reusable data points during DPCA execution and aid it to effectively transform spatial coordinate in a logical manner and reduces the time of execution of the program. The effectiveness of the proposed approach has demonstrated a high performance on the denoising of MSI images.
Cite this Article:
Rai A. Multispectral Image Denoising Using Bi-Directional Recurrent Neural Network with DPCA Algorithm. Journal of Image Processing & Pattern Recognition Progress. 2015; 2(1): 25–30p.
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