1,383 research outputs found
A Multi-Grid Iterative Method for Photoacoustic Tomography
Inspired by the recent advances on minimizing nonsmooth or bound-constrained
convex functions on models using varying degrees of fidelity, we propose a line
search multigrid (MG) method for full-wave iterative image reconstruction in
photoacoustic tomography (PAT) in heterogeneous media. To compute the search
direction at each iteration, we decide between the gradient at the target
level, or alternatively an approximate error correction at a coarser level,
relying on some predefined criteria. To incorporate absorption and dispersion,
we derive the analytical adjoint directly from the first-order acoustic wave
system. The effectiveness of the proposed method is tested on a total-variation
penalized Iterative Shrinkage Thresholding algorithm (ISTA) and its accelerated
variant (FISTA), which have been used in many studies of image reconstruction
in PAT. The results show the great potential of the proposed method in
improving speed of iterative image reconstruction
The Perception-Distortion Tradeoff
Image restoration algorithms are typically evaluated by some distortion
measure (e.g. PSNR, SSIM, IFC, VIF) or by human opinion scores that quantify
perceived perceptual quality. In this paper, we prove mathematically that
distortion and perceptual quality are at odds with each other. Specifically, we
study the optimal probability for correctly discriminating the outputs of an
image restoration algorithm from real images. We show that as the mean
distortion decreases, this probability must increase (indicating worse
perceptual quality). As opposed to the common belief, this result holds true
for any distortion measure, and is not only a problem of the PSNR or SSIM
criteria. We also show that generative-adversarial-nets (GANs) provide a
principled way to approach the perception-distortion bound. This constitutes
theoretical support to their observed success in low-level vision tasks. Based
on our analysis, we propose a new methodology for evaluating image restoration
methods, and use it to perform an extensive comparison between recent
super-resolution algorithms.Comment: CVPR 2018 (long oral presentation), see talk at:
https://youtu.be/_aXbGqdEkjk?t=39m43
RRCNN: An Enhanced Residual Recursive Convolutional Neural Network for Non-stationary Signal Decomposition
Time-frequency analysis is an important and challenging task in many
applications. Fourier and wavelet analysis are two classic methods that have
achieved remarkable success in many fields. They also exhibit limitations when
applied to nonlinear and non-stationary signals. To address this challenge, a
series of nonlinear and adaptive methods, pioneered by the empirical mode
decomposition method have been proposed. Their aim is to decompose a
non-stationary signal into quasi-stationary components which reveal better
features in the time-frequency analysis. Recently, inspired by deep learning,
we proposed a novel method called residual recursive convolutional neural
network (RRCNN). Not only RRCNN can achieve more stable decomposition than
existing methods while batch processing large-scale signals with low
computational cost, but also deep learning provides a unique perspective for
non-stationary signal decomposition. In this study, we aim to further improve
RRCNN with the help of several nimble techniques from deep learning and
optimization to ameliorate the method and overcome some of the limitations of
this technique.Comment: 8 pages, 4 figur
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