730 research outputs found
Unsupervised Multi Class Segmentation of 3D Images with Intensity Inhomogeneities
Intensity inhomogeneities in images constitute a considerable challenge in
image segmentation. In this paper we propose a novel biconvex variational model
to tackle this task. We combine a total variation approach for multi class
segmentation with a multiplicative model to handle the inhomogeneities. Our
method assumes that the image intensity is the product of a smoothly varying
part and a component which resembles important image structures such as edges.
Therefore, we penalize in addition to the total variation of the label
assignment matrix a quadratic difference term to cope with the smoothly varying
factor. A critical point of our biconvex functional is computed by a modified
proximal alternating linearized minimization method (PALM). We show that the
assumptions for the convergence of the algorithm are fulfilled by our model.
Various numerical examples demonstrate the very good performance of our method.
Particular attention is paid to the segmentation of 3D FIB tomographical images
which was indeed the motivation of our work
Learning-Based Dequantization For Image Restoration Against Extremely Poor Illumination
All existing image enhancement methods, such as HDR tone mapping, cannot
recover A/D quantization losses due to insufficient or excessive lighting,
(underflow and overflow problems). The loss of image details due to A/D
quantization is complete and it cannot be recovered by traditional image
processing methods, but the modern data-driven machine learning approach offers
a much needed cure to the problem. In this work we propose a novel approach to
restore and enhance images acquired in low and uneven lighting. First, the ill
illumination is algorithmically compensated by emulating the effects of
artificial supplementary lighting. Then a DCNN trained using only synthetic
data recovers the missing detail caused by quantization
Retinex-qDPC: automatic background rectified quantitative differential phase contrast imaging
The quality of quantitative differential phase contrast reconstruction (qDPC)
can be severely degenerated by the mismatch of the background of two oblique
illuminated images, yielding problematic phase recovery results. These
background mismatches may result from illumination patterns, inhomogeneous
media distribution, or other defocusing layers. In previous reports, the
background is manually calibrated which is time-consuming, and unstable, since
new calibrations are needed if any modification to the optical system was made.
It is also impossible to calibrate the background from the defocusing layers,
or for high dynamic observation as the background changes over time. To tackle
the mismatch of background and increases the experimental robustness, we
propose the Retinex-qDPC in which we use the images edge features as data
fidelity term yielding L2-Retinex-qDPC and L1-Retinex-qDPC for high
background-robustness qDPC reconstruction. The split Bregman method is used to
solve the L1-Retinex DPC. We compare both Retinex-qDPC models against
state-of-the-art DPC reconstruction algorithms including total-variation
regularized qDPC, and isotropic-qDPC using both simulated and experimental
data. Results show that the Retinex qDPC can significantly improve the phase
recovery quality by suppressing the impact of mismatch background. Within, the
L1-Retinex-qDPC is better than L2-Retinex and other state-of-the-art DPC
algorithms. In general, the Retinex-qDPC increases the experimental robustness
against background illumination without any modification of the optical system,
which will benefit all qDPC applications
Removing striping artifacts in light-sheet fluorescence microscopy: a review
In recent years, light-sheet fluorescence microscopy (LSFM) has found a broad application for imaging of diverse biological samples, ranging from sub-cellular structures to whole animals, both in-vivo and ex-vivo, owing to its many advantages relative to point-scanning methods. By providing the selective illumination of sample single planes, LSFM achieves an intrinsic optical sectioning and direct 2D image acquisition, with low out-of-focus fluorescence background, sample photo-damage and photo-bleaching. On the other hand, such an illumination scheme is prone to light absorption or scattering effects, which lead to uneven illumination and striping artifacts in the images, oriented along the light sheet propagation direction. Several methods have been developed to address this issue, ranging from fully optical solutions to entirely digital post-processing approaches. In this work, we present them, outlining their advantages, performance and limitations
Visibility recovery on images acquired in attenuating media. Application to underwater, fog, and mammographic imaging
136 p.When acquired in attenuating media, digital images of ten suffer from a particularly complex degradation that reduces their visual quality, hindering their suitability for further computational applications, or simply decreasing the visual pleasan tness for the user. In these cases, mathematical image processing reveals it self as an ideal tool to recover some of the information lost during the degradation process. In this dissertation,we deal with three of such practical scenarios in which this problematic is specially relevant, namely, underwater image enhancement, fogremoval and mammographic image processing. In the case of digital mammograms,X-ray beams traverse human tissue, and electronic detectorscapture them as they reach the other side. However, the superposition on a bidimensional image of three-dimensional structures produces low contraste dimages in which structures of interest suffer from a diminished visibility, obstructing diagnosis tasks. Regarding fog removal, the loss of contrast is produced by the atmospheric conditions, and white colour takes over the scene uniformly as distance increases, also reducing visibility.For underwater images, there is an added difficulty, since colour is not lost uniformly; instead, red colours decay the fastest, and green and blue colours typically dominate the acquired images. To address all these challenges,in this dissertation we develop new methodologies that rely on: a)physical models of the observed degradation, and b) the calculus of variations.Equipped with this powerful machinery, we design novel theoreticaland computational tools, including image-dependent functional energies that capture the particularities of each degradation model. These energie sare composed of different integral terms that are simultaneous lyminimized by means of efficient numerical schemes, producing a clean,visually-pleasant and use ful output image, with better contrast and increased visibility. In every considered application, we provide comprehensive qualitative (visual) and quantitative experimental results to validateour methods, confirming that the developed techniques out perform other existing approaches in the literature
Removing striping artifacts in light-sheet fluorescence microscopy: a review
In recent years, light-sheet fluorescence microscopy (LSFM) has found a broad application for imaging of diverse biological samples, ranging from sub-cellular structures to whole animals, both in-vivo and ex-vivo, owing to its many advantages relative to point-scanning methods. By providing the selective illumination of sample single planes, LSFM achieves an intrinsic optical sectioning and direct 2D image acquisition, with low out-of-focus fluorescence background, sample photo-damage and photo-bleaching. On the other hand, such an illumination scheme is prone to light absorption or scattering effects, which lead to uneven illumination and striping artifacts in the images, oriented along the light sheet propagation direction. Several methods have been developed to address this issue, ranging from fully optical solutions to entirely digital post-processing approaches. In this work, we present them, outlining their advantages, performance and limitations
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