1,201 research outputs found
SIMBA: scalable inversion in optical tomography using deep denoising priors
Two features desired in a three-dimensional (3D) optical tomographic image reconstruction algorithm are the ability to reduce imaging artifacts and to do fast processing of large data volumes. Traditional iterative inversion algorithms are impractical in this context due to their heavy computational and memory requirements. We propose and experimentally validate a novel scalable iterative mini-batch algorithm (SIMBA) for fast and high-quality optical tomographic imaging. SIMBA enables highquality imaging by combining two complementary information sources: the physics of the imaging system characterized by its forward model and the imaging prior characterized by a denoising deep neural net. SIMBA easily scales to very large 3D tomographic datasets by processing only a small subset of measurements at each iteration. We establish the theoretical fixedpoint convergence of SIMBA under nonexpansive denoisers for convex data-fidelity terms. We validate SIMBA on both simulated and experimentally collected intensity diffraction tomography (IDT) datasets. Our results show that SIMBA can significantly reduce the computational burden of 3D image formation without sacrificing the imaging quality.https://arxiv.org/abs/1911.13241First author draf
Deep Learning Based Channel Covariance Matrix Estimation with User Location and Scene Images
Channel covariance matrix (CCM) is one critical parameter for designing the
communications systems. In this paper, a novel framework of the deep learning
(DL) based CCM estimation is proposed that exploits the perception of the
transmission environment without any channel sample or the pilot signals.
Specifically, as CCM is affected by the user's movement, we design a deep
neural network (DNN) to predict CCM from user location and user speed, and the
corresponding estimation method is named as ULCCME. A location denoising method
is further developed to reduce the positioning error and improve the robustness
of ULCCME. For cases when user location information is not available, we
propose an interesting way that uses the environmental 3D images to predict the
CCM, and the corresponding estimation method is named as SICCME. Simulation
results show that both the proposed methods are effective and will benefit the
subsequent channel estimation.Comment: 30 pages, 18 figure
Diffusion Models for Medical Image Analysis: A Comprehensive Survey
Denoising diffusion models, a class of generative models, have garnered
immense interest lately in various deep-learning problems. A diffusion
probabilistic model defines a forward diffusion stage where the input data is
gradually perturbed over several steps by adding Gaussian noise and then learns
to reverse the diffusion process to retrieve the desired noise-free data from
noisy data samples. Diffusion models are widely appreciated for their strong
mode coverage and quality of the generated samples despite their known
computational burdens. Capitalizing on the advances in computer vision, the
field of medical imaging has also observed a growing interest in diffusion
models. To help the researcher navigate this profusion, this survey intends to
provide a comprehensive overview of diffusion models in the discipline of
medical image analysis. Specifically, we introduce the solid theoretical
foundation and fundamental concepts behind diffusion models and the three
generic diffusion modelling frameworks: diffusion probabilistic models,
noise-conditioned score networks, and stochastic differential equations. Then,
we provide a systematic taxonomy of diffusion models in the medical domain and
propose a multi-perspective categorization based on their application, imaging
modality, organ of interest, and algorithms. To this end, we cover extensive
applications of diffusion models in the medical domain. Furthermore, we
emphasize the practical use case of some selected approaches, and then we
discuss the limitations of the diffusion models in the medical domain and
propose several directions to fulfill the demands of this field. Finally, we
gather the overviewed studies with their available open-source implementations
at
https://github.com/amirhossein-kz/Awesome-Diffusion-Models-in-Medical-Imaging.Comment: Second revision: including more papers and further discussion
Lagrangian Recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for Secure Image Transmission
Present-day evolution in communication and information technology dispenses straightforward and effortless access to data, but the most noteworthy condition is the formation of secure communication. Numerous approaches were designed for safety communication. One of the crucial approaches is image steganography. Moreover, provisioning of information security services is arrived at via cryptosystems where cryptosystems make certain the secure messages transmission between the users in an untrustworthy circumstance. The conventional method of providing encryption and signature is said to be first signing and then encryption, but both the computation and communication costs are found to be high. A certificateless signcryption mechanism is designed to transfer the medical data or images securely. This mechanism will minimize the storage and verification costs of public key certificates. The author of this article proposes a method named Lagrangian recurrent Steganalysis and Hyper Elliptic Certificateless Signcryption for transferring the medical data or images securely. In two sections the LRS-HECS method is split. They are medical image steganalysis and certificateless signcryption. First with the Chest X-Ray images obtained as input, a Codeword Correlated Lagrangian Recurrent Neural Network-based image steganography model is applied to generate steg images. Second, to transfer the medical images securely the steg images provided as input is designed a model named a Hyper Elliptic Curve-based Certificateless Signcryption. The issue of providing the integrity and validity of the transmitted medical images and receiver anonymity is addressed by the application of Hyper Elliptic Curve. Chest X-Ray pictures were used in experimental simulations, and the findings showed that the LRS-HECS approach had more advantages over existing state-of-the-art methods in terms of higher peak signal to noise ratio with data integrity and with reduced encryption time and transmission cost
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