Results 81 to 90 of about 259,715 (355)
Unpaired Image Denoising [PDF]
2020 IEEE International Conference on Image Processing (ICIP), 2020 Deep learning approaches in image processing predominantly resort to supervised learning. A majority of methods for image denoising are no exception to this rule and hence demand pairs of noisy and corresponding clean images. Only recently has there been the emergence of methods such as Noise2Void, where a deep neural network learns to denoise solely ...
A. N. Rajagopalan, Priyatham Kattakinda
openaire +4 more sources
Machine learning denoising of high-resolution X-ray nanotomography data
Journal of Synchrotron Radiation, 2022 High-resolution X-ray nanotomography is a quantitative tool for investigating specimens from a wide range of research areas. However, the quality of the reconstructed tomogram is often obscured by noise and therefore not suitable for automatic ...
Silja Flenner +6 more
doaj +1 more source
No-reference Image Denoising Quality Assessment [PDF]
, 2018 A wide variety of image denoising methods are available now. However, the performance of a denoising algorithm often depends on individual input noisy images as well as its parameter setting.
Lu, Si
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3D Point Cloud Denoising via Deep Neural Network based Local Surface Estimation
, 2019 We present a neural-network-based architecture for 3D point cloud denoising called neural projection denoising (NPD). In our previous work, we proposed a two-stage denoising algorithm, which first estimates reference planes and follows by projecting ...
Chen, Siheng +2 more
core +1 more source
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Autosomal dominant progranulin (GRN) pathogenic variants are a genetic cause of frontotemporal lobar degeneration. Though clinical trials for GRN‐related therapies are underway, there is an unmet need for biomarkers that can predict symptom onset and track disease progression.
Taru M. Flagan +46 more
wiley +1 more source
Overview of Research on Digital Image Denoising Methods
SensorsDuring image collection, images are often polluted by noise because of imaging conditions and equipment limitations. Images are also disturbed by external noise during compression and transmission, which adversely affects consequent processing, like ...
Jing Mao +3 more
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A Fully Automated Gridding Technique for Real Composite cDNA Microarray Images
IEEE Access, 2020 Genome-wide screening using microarrays of DNA will be of great use in the early diagnosis of diseases such as cancer and HIV. It also makes use of gene discovery, pharmacogenomics, toxicogenomics, and nutrigenomics for other applications.
Steffy Maria Joseph, P. S. Sathidevi
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Relapsing–Remitting Multiple Sclerosis Is Associated With a Dysbiotic Oral Microbiome
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Multiple sclerosis (MS) is a chronic autoimmune disorder characterized by inflammation, demyelination, and neurological impairment. While the gut microbiota's role in MS is extensively studied, the association between the oral microbiota and MS remains underexplored, particularly in North American cohorts.
Sukirth M. Ganesan +12 more
wiley +1 more source
Star Sensor Denoising Algorithm Based on Edge Protection
Sensors, 2021 Single-pixel noise commonly appearing in a star sensor can cause an unexpected error in centroid extraction. To overcome this problem, this paper proposes a star image denoising algorithm, named Improved Gaussian Side Window Filtering (IGSWF).
Kaili Lu +4 more
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Boosting of Image Denoising Algorithms
, 2015 In this paper we propose a generic recursive algorithm for improving image denoising methods. Given the initial denoised image, we suggest repeating the following "SOS" procedure: (i) (S)trengthen the signal by adding the previous denoised image to the ...
Elad, Michael, Romano, Yaniv
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