Recurrent Nerual Imaging: An Evolutionary Approach for Mixed Possion-Gaussian Image Denoising

Recurrent neural networks (RNNs) are traditionally used for machine learning applications for temporal sequences such as natural language processing. Its application to image processing is relatively new. In this paper, we apply RNNs to denoise images corrupted by mixed Poisson and Gaussian noise. T...

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Veröffentlicht in:2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA) S. 484 - 489
Hauptverfasser: Ranganath, Aditya, DeGuchy, Omar, Santiago, Fabian, Singhal, Mukesh, Marcia, Roummel
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.12.2022
Schlagworte:
autoencoders
image denoising
Imaging
Knowledge engineering
Machine learning
Natural language processing
Noise measurement
Noise reduction
Recurrent neural networks
upsampling
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Zusammenfassung:Recurrent neural networks (RNNs) are traditionally used for machine learning applications for temporal sequences such as natural language processing. Its application to image processing is relatively new. In this paper, we apply RNNs to denoise images corrupted by mixed Poisson and Gaussian noise. The motivation for using an RNN comes from viewing the denoising of the Poisson-Gaussian realization as a temporal process. The network then attempts to trace back the steps that create the noisy realization in order to arrive at the noiseless reconstruction. Numerical experiments demonstrate that our proposed RNN approach outperforms convolutional autoen-coder methods for denoising and upsampling low-resolution images from the CIFAR-10 dataset.
DOI:10.1109/ICMLA55696.2022.00078