98 research outputs found
Wigner distribution transformations in high-order systems
By combining the definition of the Wigner distribution function (WDF) and the
matrix method of optical system modeling, we can evaluate the transformation of
the former in centered systems with great complexity. The effect of stops and
lens diameter are also considered and are shown to be responsible for
non-linear clipping of the resulting WDF in the case of coherent illumination
and non-linear modulation of the WDF when the illumination is incoherent. As an
example, the study of a single lens imaging systems illustrates the
applicability of the method.Comment: 16 pages, 7 figures. To appear in J. of Comp. and Appl. Mat
Explainable deep learning models in medical image analysis
Deep learning methods have been very effective for a variety of medical
diagnostic tasks and has even beaten human experts on some of those. However,
the black-box nature of the algorithms has restricted clinical use. Recent
explainability studies aim to show the features that influence the decision of
a model the most. The majority of literature reviews of this area have focused
on taxonomy, ethics, and the need for explanations. A review of the current
applications of explainable deep learning for different medical imaging tasks
is presented here. The various approaches, challenges for clinical deployment,
and the areas requiring further research are discussed here from a practical
standpoint of a deep learning researcher designing a system for the clinical
end-users.Comment: Preprint submitted to J.Imaging, MDP
OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution
There has been considerable progress in implicit neural representation to
upscale an image to any arbitrary resolution. However, existing methods are
based on defining a function to predict the Red, Green and Blue (RGB) value
from just four specific loci. Relying on just four loci is insufficient as it
leads to losing fine details from the neighboring region(s). We show that by
taking into account the semi-local region leads to an improvement in
performance. In this paper, we propose applying a new technique called
Overlapping Windows on Semi-Local Region (OW-SLR) to an image to obtain any
arbitrary resolution by taking the coordinates of the semi-local region around
a point in the latent space. This extracted detail is used to predict the RGB
value of a point. We illustrate the technique by applying the algorithm to the
Optical Coherence Tomography-Angiography (OCT-A) images and show that it can
upscale them to random resolution. This technique outperforms the existing
state-of-the-art methods when applied to the OCT500 dataset. OW-SLR provides
better results for classifying healthy and diseased retinal images such as
diabetic retinopathy and normals from the given set of OCT-A images. The
project page is available at https://rishavbb.github.io/ow-slr/index.htm
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