Blind Denoising Autoencoder

The term "blind denoising" refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for...

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Bibliographic Details
Published in:IEEE transaction on neural networks and learning systems Vol. 30; no. 1; pp. 312 - 317
Main Author: Majumdar, Angshul
Format: Journal Article
Language:English
Published: United States IEEE 01.01.2019
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Autoencoder
denoising
Dictionaries
Formulations
Learning
Machine learning
Model testing
Noise measurement
Noise reduction
Singular value decomposition
Training
Training data
Transforms
ISSN:2162-237X, 2162-2388, 2162-2388
Online Access:Get full text
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Summary:The term "blind denoising" refers to the fact that the basis used for denoising is learned from the noisy sample itself during denoising. Dictionary learning- and transform learning-based formulations for blind denoising are well known. But there has been no autoencoder-based solution for the said blind denoising approach. So far, autoencoder-based denoising formulations have learned the model on a separate training data and have used the learned model to denoise test samples. Such a methodology fails when the test image (to denoise) is not of the same kind as the models learned with. This will be the first work, where we learn the autoencoder from the noisy sample while denoising. Experimental results show that our proposed method performs better than dictionary learning (K-singular value decomposition), transform learning, sparse stacked denoising autoencoder, and the gold standard BM3D algorithm.
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ISSN:2162-237X
2162-2388
2162-2388
DOI:10.1109/TNNLS.2018.2838679