Amir has extensive experience in second-order optimization methods, image registration, inverse problems, and large scale parallel computing, developing codes that have been scaled up to 200K cores.
In this paper, we address a new approach for near-real-time enhancement of large-scale Geospatial and aerial remote sensing (RS) imagery that aggregates descriptive and Bayesian convex regularization paradigms for solving the image reconstruction inverse problems with efficient systolic-based neural network (NN) computing.
We commence with the descriptive experiment design regularization (DEDR) approach for solving the image enhancement inverse problem based on the ℓ2 -type squared error norm minimization strategy robust against the problem model uncertainties in the sense of the worst case statistical performance optimization.
(c) Filtered image after inverse Fourier transformation.
The resulting image is inverse Fourier transform, yielding I e (x, y) in spatial space.
In the real world, many important problems have reformulations which require finding common zero (fixed) points of nonlinear operators, for instance, image recovery, inverse problems, transportation problems and optimization problems.
Many important problems have reformulations which require finding solutions of classical variational inequalities, for instance, image recovery, inverse problems, transportation problems, fixed point problems and optimization problems; see [1 11] and the references therein.
(a) A schematic diagram of backside 3D diffuser lithography, (b) SEM image of inverse trapezoid with magnification (50 μm), (c) with magnification (20 μm), (d) with magnification (5 μm). Figure 15 Super-hydrophobic characterizations.
Many important problems have reformulations which require finding solutions of equilibriums (1.3) and (1.4), for instance, image recovery, inverse problems, network allocation, transportation problems and optimization problems; see [3 11] and the references therein.
