Optimal sparsity allows reliable system-aware restoration of fluorescence microscopy images.

ABSTRACT: Fluorescence microscopy is one of the most indispensable and informative driving forces for biological research, but the extent of observable biological phenomena is essentially determined by the content and quality of the acquired images. To address the different noise sources that can degrade these images, we introduce an algorithm for multiscale image restoration through optimally sparse representation (MIRO). MIRO is a deterministic framework that models the acquisition process and uses pixelwise noise correction to improve image quality. Our study demonstrates that this approach yields a remarkable restoration of the fluorescence signal for a wide range of microscopy systems, regardless of the detector used (e.g., electron-multiplying charge-coupled device, scientific complementary me

SUBMITTER: Mandracchia B 

PROVIDER: S-EPMC10468132 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature