Apr 20, 2023 · Here, we present a memristive image reconstructor (MIR) to greatly accelerate image reconstruction with discrete Fourier transformation (DFT) by computing-in-memory (CIM) with memristor...

Oct 15, 2017 · In this paper we propose a novel two dimensional (2D) Fast Fourier Transform technique for efficient reconstruction of a 2D image. The algorithm first applies 1D FFT based on radix- \(4^n\) along the rows of the image followed by same FFT operation along columns, to obtain a 2D FFT.

Jul 25, 2016 · Differential interference contrast (DIC) is a qualitative microscopy technique that shows the optical path length differences within a specimen. We propose a variational framework for DIC image...

To address this challenge, we develop a novel algorithm that combines simultaneous reconstruction-decomposition using a previously reported method for Model-Based Material Decomposition (MBMD) augmented by the Known-Component (KC) reconstruction framework to mitigate metal artifacts.

In this work we present a simple methodology to build an orthomosaic, our proposal is focused in replacing traditional digital imagen processing using instead a Convolutional Neuronal Network (CNN) model. The dataset of aerial images is generated from …

Jun 1, 2008 · In this paper a new approach to tomographic image reconstruction from projections is developed and investigated. To solve the reconstruction problem a special neural network which resembles a Hopfield net is proposed. The reconstruction process is performed during the minimizing of the energy function in this network.

Sep 26, 2024 · By seamlessly integrating these cutting-edge generative models, the paper sets out to elevate the reconstructed image’s quality, leveraging the prowess of a generative network as a visionary model.

Apr 1, 1999 · A new algorithm for the reconstruction of two-dimensional (2D) images from projections is described. The algorithm is based on the decomposition of the projections into Chebyshev polynomials of the second kind, which are the ideal basis functions for this application. The Chebyshev decomposition is done via the fast discrete sine transform.

Objective: In this paper a new approach to tomographic image reconstruction from projections is developed and investigated. Method: To solve the reconstruction problem a special neural network which resembles a Hopfield net is proposed.

To obtain high-quality CT images and decompositions, this paper proposes an iterative image reconstruction algorithm and a practical image-domain decomposition method for DECT. The reconstruction algorithm is formulated as an optimization problem, which includes total variation regularization term and data fidelity term.