Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy

Bajić, Buda; Lindblad, Joakim; Sladoje, Nataša

doi:10.1109/isbi.2016.7493226

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…Statistical noise is often minimized by traditional denoising algorithms 143,144 , including a variety of denoising algorithms developed for electron microscopy. Examples include algorithms based on block matching 145 , contourlet transforms 114,115 , energy minimization 146 , fast patch reorderings 147 , Gaussian kernel density estimation 148 , Kronecker envelope principal component analysis 149 (PCA), non-local means and Zernike moments 150 , singular value thresholding 151 , wavelets 152 , and other approaches 141,[153][154][155][156] . Noise that is not statistical is often minimized by hardware.…”

Section: Improving Signal-to-noisementioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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Deep learning is transforming most areas of science and technology, including electron microscopy. This review paper offers a practical perspective aimed at developers with limited familiarity. For context, we review popular applications of deep learning in electron microscopy. Following, we discuss hardware and software needed to get started with deep learning and interface with electron microscopes. We then review neural network components, popular architectures, and their optimization. Finally, we discuss future directions of deep learning in electron microscopy.

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Section: Improving Signal-to-noisementioning

confidence: 99%

Review: Deep Learning in Electron Microscopy

Ede

2020

Preprint

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“…On the other hand, in many situations it is difficult to accurately estimate the PSF (or the CTF) and blind methods may be preferable. B. Bajic et al [79] presented a novel restoration method for images degraded with PG noise which jointly estimates the original image and the PSF from the observed data. Although the method was not designed to process cryo-EM images, they illustrate its applicability in this field.…”

Section: Denoising and Image Restorationmentioning

confidence: 99%

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Vilas

Tabassum

Mota

et al. 2018

Current Opinion in Structural Biology

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“…33. This method is suitable for images degraded by mixed noise and enables simultaneous estimation of unknown PSF and the high quality image.…”

Section: Illustrative Example On a Naturally Degradedmentioning

confidence: 99%

Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study

Bajić

Lindblad

Sladoje

2016

J. Electron. Imaging

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Abstract. Most energy minimization-based restoration methods are developed for signal-independent Gaussian noise. The assumption of Gaussian noise distribution leads to a quadratic data fidelity term, which is appealing in optimization. When an image is acquired with a photon counting device, it contains signal-dependent Poisson or mixed Poisson-Gaussian noise. We quantify the loss in performance that occurs when a restoration method suited for Gaussian noise is utilized for mixed noise. Signal-dependent noise can be treated by methods based on either classical maximum a posteriori (MAP) probability approach or on a variance stabilization approach (VST). We compare performances of these approaches on a large image material and observe that VST-based methods outperform those based on MAP in both quality of restoration and in computational efficiency. We quantify improvement achieved by utilizing Huber regularization instead of classical total variation regularization. The conclusion from our study is a recommendation to utilize a VST-based approach combined with regularization by Huber potential for restoration of images degraded by blur and signal-dependent noise. This combination provides a robust and flexible method with good performance and high speed.

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Blind restoration of images degraded with mixed poisson-Gaussian noise with application in transmission electron microscopy

Cited by 12 publications

References 20 publications

Review: Deep Learning in Electron Microscopy

Review: Deep Learning in Electron Microscopy

Advances in image processing for single-particle analysis by electron cryomicroscopy and challenges ahead

Restoration of images degraded by signal-dependent noise based on energy minimization: an empirical study

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