20
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      DVDeconv: An Open-Source MATLAB Toolbox for Depth-Variant Asymmetric Deconvolution of Fluorescence Micrographs

      research-article
      , ,
      Cells
      MDPI
      3D microscopy, fluorescence microscopy, deconvolution, blind deconvolution

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          To investigate the cellular structure, biomedical researchers often obtain three-dimensional images by combining two-dimensional images taken along the z axis. However, these images are blurry in all directions due to diffraction limitations. This blur becomes more severe when focusing further inside the specimen as photons in deeper focus must traverse a longer distance within the specimen. This type of blur is called depth-variance. Moreover, due to lens imperfection, the blur has asymmetric shape. Most deconvolution solutions for removing blur assume depth-invariant or x-y symmetric blur, and presently, there is no open-source for depth-variant asymmetric deconvolution. In addition, existing datasets for deconvolution microscopy also assume invariant or x-y symmetric blur, which are insufficient to reflect actual imaging conditions. DVDeconv, that is a set of MATLAB functions with a user-friendly graphical interface, has been developed to address depth-variant asymmetric blur. DVDeconv includes dataset, depth-variant asymmetric point spread function generator, and deconvolution algorithms. Experimental results using DVDeconv reveal that depth-variant asymmetric deconvolution using DVDeconv removes blurs accurately. Furthermore, the dataset in DVDeconv constructed can be used to evaluate the performance of microscopy deconvolution to be developed in the future.

          Related collections

          Most cited references33

          • Record: found
          • Abstract: found
          • Article: not found

          Surpassing the lateral resolution limit by a factor of two using structured illumination microscopy.

          Lateral resolution that exceeds the classical diffraction limit by a factor of two is achieved by using spatially structured illumination in a wide-field fluorescence microscope. The sample is illuminated with a series of excitation light patterns, which cause normally inaccessible high-resolution information to be encoded into the observed image. The recorded images are linearly processed to extract the new information and produce a reconstruction with twice the normal resolution. Unlike confocal microscopy, the resolution improvement is achieved with no need to discard any of the emission light. The method produces images of strikingly increased clarity compared to both conventional and confocal microscopes.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Content-aware image restoration: pushing the limits of fluorescence microscopy

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Deep learning enables cross-modality super-resolution in fluorescence microscopy

              We present deep-learning-enabled super-resolution across different fluorescence microscopy modalities. This data-driven approach does not require numerical modeling of the imaging process or the estimation of a point-spread-function, and is based on training a generative adversarial network (GAN) to transform diffraction-limited input images into super-resolved ones. Using this framework, we improve the resolution of wide-field images acquired with low-numerical-aperture objectives, matching the resolution that is acquired using high-numerical-aperture objectives. We also demonstrate cross-modality super-resolution, transforming confocal microscopy images to match the resolution acquired with a stimulated emission depletion (STED) microscope. We further demonstrate that total internal reflection fluorescence (TIRF) microscopy images of subcellular structures within cells and tissues can be transformed to match the results obtained with a TIRF-based structured illumination microscope. The deep network rapidly outputs these super-resolved images, without any iterations or parameter search, and could serve to democratize super-resolution imaging.
                Bookmark

                Author and article information

                Contributors
                Role: Academic Editor
                Role: Academic Editor
                Role: Academic Editor
                Journal
                Cells
                Cells
                cells
                Cells
                MDPI
                2073-4409
                15 February 2021
                February 2021
                : 10
                : 2
                : 397
                Affiliations
                Robot R&D Group, Factory Automation Technology Team, Global Technology Center, Samsung Electronics, 129, Samsung-ro, Yeongtong, Suwon 443-742, Gyeonggi, Korea; by1110.kim@ 123456samsung.com
                Article
                cells-10-00397
                10.3390/cells10020397
                7919057
                bc4df5d3-ea09-44c2-bc3b-f2a1fc34052a
                © 2021 by the author.

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                : 03 January 2021
                : 11 February 2021
                Categories
                Article

                3d microscopy,fluorescence microscopy,deconvolution,blind deconvolution

                Comments

                Comment on this article