Gio
07
Mar
Seminari e Convegni
Scattering networks and singular values decomposition: different methods to remove background in single-molecule localization microscopy images
The event entitled "Scattering networks and singular values decomposition: different methods to remove background in single-molecule localization microscopy images" will be held on Thursday 7 March 2024 - in the Aula Buzano hall of the Politecnico.
Abstract
In optical image formation, a major challenge in Single-Molecule Localization Microscopy (SMLM) is the presence of background noise, which degrades image quality and contrast. This arises from an overlap of sparse, localized molecules with a fixed background. To address this issue, we explore two methods: the Scattering Network and Singular Value Decomposition (SVD). The Scattering Network offers a translation-invariant image representation, which is stable to deformations, achieved through fixed wavelet filters in a deep Convolutional Neural Network (CNN) architecture. This representation has several advantages, such as low computational requirements and interpretability, making it ideal for SMLM. However, it cannot take into account the temporal information present in SMLM datasets. To include dynamic information, we propose SVD as a spatial-temporal representation. SVD decomposes the images into temporal and spatial components, which are combined and weighted by singular values. By focusing on components associated with smaller singular values, known to be related to molecules, we effectively filter out background noise.
Speaker: Lisa Cuneo - Istituto Italiano di Tecnologia (Genova)
Abstract
In optical image formation, a major challenge in Single-Molecule Localization Microscopy (SMLM) is the presence of background noise, which degrades image quality and contrast. This arises from an overlap of sparse, localized molecules with a fixed background. To address this issue, we explore two methods: the Scattering Network and Singular Value Decomposition (SVD). The Scattering Network offers a translation-invariant image representation, which is stable to deformations, achieved through fixed wavelet filters in a deep Convolutional Neural Network (CNN) architecture. This representation has several advantages, such as low computational requirements and interpretability, making it ideal for SMLM. However, it cannot take into account the temporal information present in SMLM datasets. To include dynamic information, we propose SVD as a spatial-temporal representation. SVD decomposes the images into temporal and spatial components, which are combined and weighted by singular values. By focusing on components associated with smaller singular values, known to be related to molecules, we effectively filter out background noise.
Speaker: Lisa Cuneo - Istituto Italiano di Tecnologia (Genova)