Real-time image denoising of mixed Poisson–Gaussian noise in fluorescence microscopy images using ImageJ
Fluorescence microscopy imaging speed is fundamentally limited by the measurement
signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate …
signal-to-noise ratio (SNR). To improve image SNR for a given image acquisition rate …
Toward ground-truth optical coherence tomography via three-dimensional unsupervised deep learning processing and data
Optical coherence tomography (OCT) can perform non-invasive high-resolution three-
dimensional (3D) imaging and has been widely used in biomedical fields, while it is …
dimensional (3D) imaging and has been widely used in biomedical fields, while it is …
[PDF][PDF] ANIMAL-CLEAN–A Deep Denoising Toolkit for Animal-Independent Signal Enhancement
Signal enhancement in bioacoustics can be of vital importance due to the fact that
recordings are largely done in noise-heavy environments, in which anthrophonic …
recordings are largely done in noise-heavy environments, in which anthrophonic …
Shot noise reduction in radiographic and tomographic multi-channel imaging with self-supervised deep learning
Shot noise is a critical issue in radiographic and tomographic imaging, especially when
additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper …
additional constraints lead to a significant reduction of the signal-to-noise ratio. This paper …
Low-dose CT reconstruction by self-supervised learning in the projection domain
In the intention of minimizing excessive X-ray radiation administration to patients, low-dose
computed tomography (LDCT) has become a distinct trend in radiology. However, while …
computed tomography (LDCT) has become a distinct trend in radiology. However, while …
Selfredepth: Self-supervised real-time depth restoration for consumer-grade sensors
Depth maps produced by consumer-grade sensors suffer from inaccurate measurements
and missing data from either system or scene-specific sources. Data-driven denoising …
and missing data from either system or scene-specific sources. Data-driven denoising …
Self-supervised Single-Image Deconvolution with Siamese Neural Networks
Inverse problems in image reconstruction are fundamentally complicated by unknown noise
properties. Classical iterative deconvolution approaches amplify noise and require careful …
properties. Classical iterative deconvolution approaches amplify noise and require careful …
Self-supervised denoising of Nyquist-sampled volumetric images via deep learning
Purpose Deep learning has demonstrated excellent performance enhancing noisy or
degraded biomedical images. However, many of these models require access to a noise …
degraded biomedical images. However, many of these models require access to a noise …