Solving the boundary artifact for the enhanced deconvolution algorithm SUPPOSe applied to fluorescence microscopy

Abstract

The SUPPOSe enhanced deconvolution algorithm relies in assuming that the image source can be described by an incoherent superposition of virtual point sources of equal intensity and finding the number and position of such virtual sources. In this work we describe the recent advances in the implementation of the method to gain resolution and remove artifacts due to the presence of fluorescent molecules close enough to the image frame boundary. The method was modified removing the invariant used before given by the product of the flux of the virtual sources times the number of virtual sources, and replacing it by a new invariant given by the total flux within the frame, thus allowing the location of virtual sources outside the frame but contributing to the signal inside the frame.

Synergistic Classification of Multilevel Land Patches (SC-MLPs): Reducing Conflicts and Improving Mapping Results for Land Uses and Functional Spaces With Very-High-Resolution Satellite Imagery

“…To resolve this issue, state-of-the-art deep learning methods have been widely applied to land-use classifications in recent several years [32], as deep learning methods can automatically extract robust, representative, and abstract features of land uses, reducing the heterogeneity and improving the classification accuracy [33], [34], [35]. However, deep-learning semantic segmentation methods, such as RefineNet, PSPNet, and DeepLabv3+ [36], [37], may exacerbate the ambiguity of land-use boundaries due to the deconvolution and upsampling processes [38], leading to inaccurate boundaries of land uses.…”

Improving STED microscopy with SUPPOSe: enhancing resolution from a single-image

“…Other methods may also impose conditions on the sample to avoid artifacts or to improve the robustness in the presence of noise, background and other experimental conditions. In this sense, the SUPPOSe method is a convolution-based algorithm for improving microscopy images that relies on representing any object under test as a superposition of virtual point sources, all with the same intensity [8][9][10][11]. By knowing the image formation process, SUPPOSe solves an optimization problem that retrieves the optimum set of positions of the virtual sources and their intensity from a single image, resulting in a description of the object with a resolution that is more than three times better the instrument resolution under normal measurement conditions [12,13].…”

Improving the resolution of STED microscopy using the SUPPOSe algorithm

“…In fluorescence microscopy, each acquired sample is the result of a noise process acting on the convolution between an underlying object -an arrangement of fluorescent proteins tied to the things we want to see-with the microscope response function -known as Point Spread Function or PSF. SUPPOSe is a convolution-based algorithm for improving microscopy images that relies on representing the object under microscope as a SUPperposition of POint SourcEs with the same intensity [1][2][3][4][5][6][7][8]. By knowing the instrument Point-Spread Function (PSF) and the image formation model, the optimum position of these sources can be retrieved by iteratively solving an optimization problem that results in a description of the object with better resolution than the image itself.…”