Image Denoising with Diversity Enhanced Conditional GAN
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Author / Producer
Date
2024
Publication Type
Conference Paper
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yes
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Abstract
Image denoising is an inverse problem with many possible solutions. There have been many attempts to solve this problem, some producing single output, some a distribution of possible images. Among others, methods based on diffusion models and Generative Adversarial Networks (GANs) generate high-quality denoised images. GANs are particularly useful in high throughput applications due to their computational efficiency. Still, they often suffer from mode collapse and cannot provide diverse distributions of denoised images. In this work, we present a simple variance-enhanced conditional GAN denoising model. Training with additional mean stabilizing and variance-enhancing loss terms results in a model which outperforms standard methods on three simple data sets and is robust to the hyperparameter tuning. We show that this architecture is well suited for (high-throughput) setups where only noisy target images are present e.g. single particle electron microscopy (EM) image denoising. Finally, we show that in the case of noisy targets, best-denoised predictions can be selected by posterior sampling to enhance further processing steps.
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Publication status
published
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Book title
Intelligent Computing
Journal / series
Volume
1016
Pages / Article No.
372 - 382
Publisher
Springer
Event
Computing Conference 2024
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Software
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Subject
Denoising; Noise-to-noise; Image restoration; GANs