Download Compensated lens-free light field microscopy

Compensated lens-free light field microscopy
A. Boroomand1 , M.J. Shafiee1 , L. Wang1 , E. Kuang1 , F. Kazemzadeh1 , A. Wong1
1
University of Waterloo, Waterloo, Canada
Corresponding author: aborooma@uwaterloo.ca
Lens-free light field microscopy (LLFM) is a holographic microscope with large field of view
which produces an image of micro-range resolution using an interferometric reconstruction
method [1]. The quality of LLFM imaging is limited due to the effect of image degradation
factors such as (1) optical aberrations caused by the detector, (2) illumination non-uniformities
and (3) imaging noise, all need to be corrected to improve the overall quality of LLFM imaging.
Here, we propose a Bayesian based computational method to produce a compensated LLFM
(C-LLFM) image by jointly correcting for (1) optical aberrations, (2) illumination non-uniformities
and (3) imaging noise. With modeling both LLFM measurement m = {mi , iS} and C-LLFM
image f = {fi , iS} in space S, C-LLFM imaging is formulated as an inverse problem in a
maximum a posteriori (MAP) framework,
!
Q
Q
m
−D(f
,a,b)
kNi −Nj kp
i
(D(fi ,a,b)) i e
fˆ = arg max
exp −exp(
)(fi − fj )
(1)
mi !
σ
f
i∈S
(i,j)c,c∈S
where, D(.) is a degraded inverse diffraction function. The MAP framework of Eq. (1) takes
advantage of a stochastically fully-connected conditional random field model (SFCRF) [2] to
produce a C-LLFM image fˆ from LLFM measurement m by jointly correcting for the optical aberrations a, illumination non-uniformities b and imaging noise in a unified compensation
framework. Here, SFCRF is used to control the imaging noise by accounting for full-range
interactions between the whole measurement m and the desired C-LLFM image f using predefined stochastic clique structures c defined is space S.
Fig. 1(a) shows a sample LLFM measurement from a selected region of a resolution target
including multiple horizontal and vertical bars of 2µm width and 2µm spacing, captured using
a 470 nm wavelength LLFM instrument. An interferometric reconstruction of LLFM measurement is shown in Fig. 1(b) and the produced C-LLFM image using the proposed approach
is shown in Fig. 1(c). Comparison of images in Fig. 1(b and c) and the displayed intensity
plots show the efficacy of proposed method for the quality enhancement of LLFM imaging. CLLFM image in (c) shows better spatial resolution such that leads to the easier distinguishing
of the bars from each other, less illumination non-uniformities and also higher image contrast
due to the effective imaging noise suppression.
Figure 1: Selected region of resolution target: (a) LLFM measurement, (b) interferometric
reconstruction of LLFM and (c) C-LLFM image.
In conclusion, the proposed C-LLFM approach improved the quality of LLFM imaging in terms
of spatial resolution and image contrast such that the micrometer range features of the imaged
specimen/sample can be better preserved and observed in the produced C-LLFM image.
[1] F. kazemzadeh, et. al., Optics letters, 40 (2015), No.16.
[2] M.J Shafiee, et. al., ICIP, 42894293 (2014).
Keywords: Lens-free light field microscope, inverse problem, stochastically fully connected conditional random
field, quality enhancement.