The Experts below are selected from a list of 133128 Experts worldwide ranked by ideXlab platform

Scott Howard - One of the best experts on this subject based on the ideXlab platform.

  • A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images
    2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    Co-Authors: Yide Zhang, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, Scott Howard
    Abstract:

    Fluorescence Microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, Fluorescence Microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean Fluorescence Microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise Fluorescence Microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real Fluorescence Microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real Microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.

David A. Clausi - One of the best experts on this subject based on the ideXlab platform.

  • EMBC - Saliency-guided compressive Fluorescence Microscopy
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Shimon Schwartz, Alexander Wong, David A. Clausi
    Abstract:

    A novel saliency-guided approach is proposed for improving the acquisition speed of compressive Fluorescence Microscopy systems. By adaptively optimizing the sampling probability density based on regions of interest instead of the traditional unguided random sampling approach, the proposed saliency-guided compressive Fluorescence Microscopy approach can achieve high-quality Microscopy images using less than half of the number of Fluorescence Microscopy data measurements required by existing compressive Fluorescence Microscopy systems to achieve the same level of quality.

  • Saliency-guided compressive Fluorescence Microscopy
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Shimon Schwartz, Alexander Wong, David A. Clausi
    Abstract:

    A novel saliency-guided approach is proposed for improving the acquisition speed of compressive Fluorescence Microscopy systems. By adaptively optimizing the sampling probability density based on regions of interest instead of the traditional unguided random sampling approach, the proposed saliency-guided compressive Fluorescence Microscopy approach can achieve high-quality Microscopy images using less than half of the number of Fluorescence Microscopy data measurements required by existing compressive Fluorescence Microscopy systems to achieve the same level of quality.

Yide Zhang - One of the best experts on this subject based on the ideXlab platform.

  • A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images
    2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    Co-Authors: Yide Zhang, Evan Nichols, Qingfei Wang, Siyuan Zhang, Cody Smith, Scott Howard
    Abstract:

    Fluorescence Microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, Fluorescence Microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean Fluorescence Microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise Fluorescence Microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real Fluorescence Microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real Microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.

  • CVPR - A Poisson-Gaussian Denoising Dataset With Real Fluorescence Microscopy Images
    2019 IEEE CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
    Co-Authors: Yide Zhang, Qingfei Wang, Siyuan Zhang, Evan L. Nichols, Cody J. Smith, Scott S. Howard
    Abstract:

    Fluorescence Microscopy has enabled a dramatic development in modern biology. Due to its inherently weak signal, Fluorescence Microscopy is not only much noisier than photography, but also presented with Poisson-Gaussian noise where Poisson noise, or shot noise, is the dominating noise source. To get clean Fluorescence Microscopy images, it is highly desirable to have effective denoising algorithms and datasets that are specifically designed to denoise Fluorescence Microscopy images. While such algorithms exist, no such datasets are available. In this paper, we fill this gap by constructing a dataset - the Fluorescence Microscopy Denoising (FMD) dataset - that is dedicated to Poisson-Gaussian denoising. The dataset consists of 12,000 real Fluorescence Microscopy images obtained with commercial confocal, two-photon, and wide-field microscopes and representative biological samples such as cells, zebrafish, and mouse brain tissues. We use image averaging to effectively obtain ground truth images and 60,000 noisy images with different noise levels. We use this dataset to benchmark 10 representative denoising algorithms and find that deep learning methods have the best performance. To our knowledge, this is the first real Microscopy image dataset for Poisson-Gaussian denoising purposes and it could be an important tool for high-quality, real-time denoising applications in biomedical research.

Xiaowei Zhuang - One of the best experts on this subject based on the ideXlab platform.

  • super resolution Fluorescence Microscopy
    Annual Review of Biochemistry, 2009
    Co-Authors: Bo Huang, Mark Bates, Xiaowei Zhuang
    Abstract:

    Achieving a spatial resolution that is not limited by the diffraction of light, recent developments of super-resolution Fluorescence Microscopy techniques allow the observation of many biological structures not resolvable in conventional Fluorescence Microscopy. New advances in these techniques now give them the ability to image three-dimensional (3D) structures, measure interactions by multicolor colocalization, and record dynamic processes in living cells at the nanometer scale. It is anticipated that super-resolution Fluorescence Microscopy will become a widely used tool for cell and tissue imaging to provide previously unobserved details of biological structures and processes.

Shimon Schwartz - One of the best experts on this subject based on the ideXlab platform.

  • EMBC - Saliency-guided compressive Fluorescence Microscopy
    Conference proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and, 2012
    Co-Authors: Shimon Schwartz, Alexander Wong, David A. Clausi
    Abstract:

    A novel saliency-guided approach is proposed for improving the acquisition speed of compressive Fluorescence Microscopy systems. By adaptively optimizing the sampling probability density based on regions of interest instead of the traditional unguided random sampling approach, the proposed saliency-guided compressive Fluorescence Microscopy approach can achieve high-quality Microscopy images using less than half of the number of Fluorescence Microscopy data measurements required by existing compressive Fluorescence Microscopy systems to achieve the same level of quality.

  • Saliency-guided compressive Fluorescence Microscopy
    2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2012
    Co-Authors: Shimon Schwartz, Alexander Wong, David A. Clausi
    Abstract:

    A novel saliency-guided approach is proposed for improving the acquisition speed of compressive Fluorescence Microscopy systems. By adaptively optimizing the sampling probability density based on regions of interest instead of the traditional unguided random sampling approach, the proposed saliency-guided compressive Fluorescence Microscopy approach can achieve high-quality Microscopy images using less than half of the number of Fluorescence Microscopy data measurements required by existing compressive Fluorescence Microscopy systems to achieve the same level of quality.