Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement

Bajić, Buda; Suveer, Amit; Gupta, Anindya; Pepic, Ivana; Lindblad, Joakim; Sladoje, Nataša; Sintorn, Ida‐Maria

doi:10.1109/isbi.2018.8363721

Cited by 5 publications

(11 citation statements)

References 14 publications

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“…Noise is typically generated when LM is employed to capture images. Moreover, in the biomedical field where cells and tissues with complex structures and functions are analyzed, the presence of noise is fatal [ 33 , 34 ]. Although various conventional algorithms have been developed to reduce and solve noise problems, these processes have deteriorated the sharpness and resolution of LM images [ 35 , 36 , 37 ].…”

Section: Discussionmentioning

confidence: 99%

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

Kang

Kim

2021

IJERPH

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The purpose of this study is to evaluate the various control parameters of a modeled fast non-local means (FNLM) noise reduction algorithm which can separate color channels in light microscopy (LM) images. To achieve this objective, the tendency of image characteristics with changes in parameters, such as smoothing factors and kernel and search window sizes for the FNLM algorithm, was analyzed. To quantitatively assess image characteristics, the coefficient of variation (COV), blind/referenceless image spatial quality evaluator (BRISQUE), and natural image quality evaluator (NIQE) were employed. When high smoothing factors and large search window sizes were applied, excellent COV and unsatisfactory BRISQUE and NIQE results were obtained. In addition, all three evaluation parameters improved as the kernel size increased. However, the kernel and search window sizes of the FNLM algorithm were shown to be dependent on the image processing time (time resolution). In conclusion, this work has demonstrated that the FNLM algorithm can effectively reduce noise in LM images, and parameter optimization is important to achieve the algorithm’s appropriate application.

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Section: Discussionmentioning

confidence: 99%

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

Kang

Kim

2021

IJERPH

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“…Excluding VGG16, other CNN models can still leverage the advantages of TL. One example includes deep CNNs, such as the InceptionV3 [76] and ResNet50 pre-trained on the ImageNet database by et al [212] However, the authors remove the first and last fully connected layer of the pre-trained models since the similarity of the TEM dataset and the ImageNet is low, and the networks are re-trained on a very small TEM dataset provided by the authors, which only has 190 TEM images and the average peak-SNR(PSNR) [213] is approximately 22.25. Empirical interventions like this are not desirable, but the networks are eventually validated on a small test dataset with 21 TEM images and were faster than training a new model from scratch.…”

Section: Applications Of Transfer Learningmentioning

confidence: 99%

“…This includes SEM [69] and x-ray tomographic [72] microscopy. DL can be used to remove noise from TEM images, which can improve image quality and facilitate downstream analysis [73][74][75][76][77][78][79][80][81]. DL-based denoising has been applied to SEM [82][83][84] and tomography [85,86] as well.…”

Section: Introductionmentioning

confidence: 99%

Advancing electron microscopy using deep learning

Chen,

Barnard

2024

J. Phys. Mater.

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Electron microscopy, a sub-field of microanalysis, is a victim of its own success. The widespread use of electron microscopy for imaging molecules and materials has had an enormous impact on our understanding of countless systems and has accelerated impacts in drug discovery and materials design, for electronic, energy, environment and health applications. With this success a bottleneck has emerged, as the rate at which we can collect data has significantly exceeded the rate at which we can analyse it. Fortunately, this has coincided with the rise of advanced computational methods, including data science and machine learning. Deep learning, a sub-field of machine learning capable of learning from large quantities of data such as images, is ideally suited to overcome some of the challenges of electron microscopy at scale. There are a variety of different deep learning approaches relevant to the field, with unique advantages and disadvantages. In this review, we describe some well-established methods, with some recent examples, and introduce some new methods currently emerging in computer science. Our summary of deep learning is designed to guide electron microscopists to choose the right deep learning algorithm for their research and prepare for their digital future.

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“…As second (more established) benchmark, we considered denoising short exposure TEM images as discussed by Bajic et al [42]. The dataset contains a sequence of noisy short exposure images showing the same scene.…”

Section: Denoising Of Short-exposure Tem Images Of Ciliamentioning

confidence: 99%

“…With this paper, we aim at evaluating latest developments in the field of unsupervised image denoising using recent microscopy imaging datasets including, most prominently, very recent TEM images of SARS-CoV-2 infection scenes [41], TEM images of cilia [42], as well as fluorescence microscopy images of a recent benchmark on unsupervised denoising with deep generative models [30,43] (details under Datasets). The concrete algorithms we here consider are (see Algorithms for details and Table 1 for an overview): median filtering (compare, e.g., [8]), BM3D [12] in conjunction with an auxiliary blind noise level estimator [44], BM3D with variance stabilizing transformation (VST+BM3D [14]), iterative BM3D with variance stabilizing transformation (I+VST+BM3D [15]), N2V, Noise2Fast (N2F [31]), Self2Self (S2S [29]), DivNoising (DivN [30]), Evolutionary Spike-and-Slab Sparse Coding (ES3C [45]), and a Poisson Mixture Model (PMM; compare e.g.…”

Section: Introductionmentioning

confidence: 99%

Zero-shot denoising of microscopy images recorded at high-resolution limits

Salwig,

Drefs,

Lücke

2024

PLoS Comput Biol

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Conventional and electron microscopy visualize structures in the micrometer to nanometer range, and such visualizations contribute decisively to our understanding of biological processes. Due to different factors in recording processes, microscopy images are subject to noise. Especially at their respective resolution limits, a high degree of noise can negatively effect both image interpretation by experts and further automated processing. However, the deteriorating effects of strong noise can be alleviated to a large extend by image enhancement algorithms. Because of the inherent high noise, a requirement for such algorithms is their applicability directly to noisy images or, in the extreme case, to just a single noisy image without a priori noise level information (referred to as blind zero-shot setting). This work investigates blind zero-shot algorithms for microscopy image denoising. The strategies for denoising applied by the investigated approaches include: filtering methods, recent feed-forward neural networks which were amended to be trainable on noisy images, and recent probabilistic generative models. As datasets we consider transmission electron microscopy images including images of SARS-CoV-2 viruses and fluorescence microscopy images. A natural goal of denoising algorithms is to simultaneously reduce noise while preserving the original image features, e.g., the sharpness of structures. However, in practice, a tradeoff between both aspects often has to be found. Our performance evaluations, therefore, focus not only on noise removal but set noise removal in relation to a metric which is instructive about sharpness. For all considered approaches, we numerically investigate their performance, report their denoising/sharpness tradeoff on different images, and discuss future developments. We observe that, depending on the data, the different algorithms can provide significant advantages or disadvantages in terms of their noise removal vs. sharpness preservation capabilities, which may be very relevant for different virological applications, e.g., virological analysis or image segmentation.

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Denoising of short exposure transmission electron microscopy images for ultrastructural enhancement

Cited by 5 publications

References 14 publications

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

Application of Fast Non-Local Means Algorithm for Noise Reduction Using Separable Color Channels in Light Microscopy Images

Advancing electron microscopy using deep learning

Zero-shot denoising of microscopy images recorded at high-resolution limits

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