Tomographic diffractive microscopy with agile illuminations for imaging targets in a noisy background

Abstract

Tomographic diffractive microscopy is a marker-free optical digital imaging technique in which three-dimensional samples are reconstructed from a set of holograms recorded under different angles of incidence. We show experimentally that, by processing the holograms with singular value decomposition, it is possible to image objects in a noisy background that are invisible with classical wide-field microscopy and conventional tomographic reconstruction procedure. The targets can be further characterized with a selective quantitative inversion.

Fast far-field calculation in the discrete dipole approximation

“…The computation of the far-field using Fast Fourier transforms is particularly well-adapted to applications in microscopy or holography [12] as it yields the scattered far-field on a rectangular grid of transverse wavevectors ðk x ; k y Þ which is exactly what is obtained on the pixels of a camera. This sampling is also very efficient for estimating the scattering pattern of an object as it paves quite regularly the observation sphere.…”

“…For large weakly contrasted objects it can even last longer than the calculation of the field inside the object. This situation is encountered for example in optical microscopy, in holography [12][13][14], or in flow cytometry [15] applications, which require to calculate the far-field of soft optical objects (generally biological samples) on a large grid of scattering angles. Thus, improving the DDA far-field computation is now necessary for extending the application domain of this approach.…”

Tomographic Diffractive Microscopy: A Review of Methods and Recent Developments

“…One reported integrating DORT (décomposition de l'opérateur de retournement temporal) and SVD (single value decomposition) methods to quickly localize the sample from a noisy environment. This not only improves the resolution but also ameliorates the reconstruction speed [53,86]. Furthermore, if prior information of a sample, such as an estimation or range of sample permittivity, is available, the resolution can be improved [10].…”

Far-field diffraction microscopy at λ/10 resolution

“…To reduce the influence of the speckle noise, we processed the data with an efficient approach based on the singular value decomposition of the scattering matrix [28]. Basically, we formed linear combinations of f mes l;m in order to generate the target responses to specific illuminations that are focused on the target; see Supplement 1 for more detail.…”