Search Results for author: Saiprasad Ravishankar

Found 37 papers, 8 papers with code

Combining Deep Learning and Adaptive Sparse Modeling for Low-dose CT Reconstruction

no code implementations19 May 2022 Ling Chen, Zhishen Huang, Yong Long, Saiprasad Ravishankar

Recent application of deep learning methods for image reconstruction provides a successful data-driven approach to addressing the challenges when reconstructing images with measurement undersampling or various types of noise.

Computed Tomography (CT) Image Reconstruction

Multi-layer Clustering-based Residual Sparsifying Transform for Low-dose CT Image Reconstruction

no code implementations22 Mar 2022 Xikai Yang, Zhishen Huang, Yong Long, Saiprasad Ravishankar

In this study, we propose a network-structured sparsifying transform learning approach for X-ray computed tomography (CT), which we refer to as multi-layer clustering-based residual sparsifying transform (MCST) learning.

Computed Tomography (CT) Image Reconstruction

Learning Nonlocal Sparse and Low-Rank Models for Image Compressive Sensing

no code implementations17 Mar 2022 Zhiyuan Zha, Bihan Wen, Xin Yuan, Saiprasad Ravishankar, Jiantao Zhou, Ce Zhu

Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.

Compressive Sensing

Sparse-view Cone Beam CT Reconstruction using Data-consistent Supervised and Adversarial Learning from Scarce Training Data

no code implementations23 Jan 2022 Anish Lahiri, Marc Klasky, Jeffrey A. Fessler, Saiprasad Ravishankar

This work focuses on image reconstruction in such settings, i. e., when both the number of available CT projections and the training data is extremely limited.

3D Reconstruction Image Reconstruction

Bilevel learning of l1-regularizers with closed-form gradients(BLORC)

no code implementations21 Nov 2021 Avrajit Ghosh, Michael T. McCann, Saiprasad Ravishankar

We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems.

Bilevel Optimization Denoising

Single-pass Object-adaptive Data Undersampling and Reconstruction for MRI

1 code implementation17 Nov 2021 Zhishen Huang, Saiprasad Ravishankar

There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements.

Image Reconstruction

Physics-Driven Learning of Wasserstein GAN for Density Reconstruction in Dynamic Tomography

1 code implementation28 Oct 2021 Zhishen Huang, Marc Klasky, Trevor Wilcox, Saiprasad Ravishankar

Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications.

Time Series

Blind Primed Supervised (BLIPS) Learning for MR Image Reconstruction

1 code implementation11 Apr 2021 Anish Lahiri, Guanhua Wang, Saiprasad Ravishankar, Jeffrey A. Fessler

We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.

Dictionary Learning Image Reconstruction

Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond

no code implementations26 Mar 2021 Zhishen Huang, Siqi Ye, Michael T. McCann, Saiprasad Ravishankar

Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.

Dictionary Learning Image Reconstruction

Local Models for Scatter Estimation and Descattering in Polyenergetic X-Ray Tomography

no code implementations11 Dec 2020 Michael T. McCann, Marc L. Klasky, Jennifer L. Schei, Saiprasad Ravishankar

To estimate scatter for a new radiograph, we adaptively fit a scatter model to a small subset of the training data containing the radiographs most similar to it.

Computed Tomography (CT)

Two-layer clustering-based sparsifying transform learning for low-dose CT reconstruction

no code implementations1 Nov 2020 Xikai Yang, Yong Long, Saiprasad Ravishankar

Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings.

Image Reconstruction

Multi-layer Residual Sparsifying Transform (MARS) Model for Low-dose CT Image Reconstruction

no code implementations10 Oct 2020 Xikai Yang, Yong Long, Saiprasad Ravishankar

In this work, we develop a new image reconstruction approach based on a novel multi-layer model learned in an unsupervised manner by combining both sparse representations and deep models.

Image Reconstruction SSIM

Unified Supervised-Unsupervised (SUPER) Learning for X-ray CT Image Reconstruction

no code implementations6 Oct 2020 Siqi Ye, Zhipeng Li, Michael T. McCann, Yong Long, Saiprasad Ravishankar

The proposed learning formulation combines both unsupervised learning-based priors (or even simple analytical priors) together with (supervised) deep network-based priors in a unified MBIR framework based on a fixed point iteration analysis.

Computed Tomography (CT) Image Reconstruction

Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration

1 code implementation24 Jun 2020 Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar, Bihan Wen

Experimental results demonstrate that (1) Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images.

Denoising Image Reconstruction +1

SUPER Learning: A Supervised-Unsupervised Framework for Low-Dose CT Image Reconstruction

no code implementations26 Oct 2019 Zhipeng Li, Siqi Ye, Yong Long, Saiprasad Ravishankar

Recent works have shown the promising reconstruction performance of methods such as PWLS-ULTRA that rely on clustering the underlying (reconstructed) image patches into a learned union of transforms.

Image Reconstruction Image Restoration

Two-layer Residual Sparsifying Transform Learning for Image Reconstruction

no code implementations1 Jun 2019 Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Marc Louis Klasky, Brendt Wohlberg

Signal models based on sparsity, low-rank and other properties have been exploited for image reconstruction from limited and corrupted data in medical imaging and other computational imaging applications.

Image Reconstruction

Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning

no code implementations4 Apr 2019 Saiprasad Ravishankar, Jong Chul Ye, Jeffrey A. Fessler

This paper focuses on the two most recent trends in medical image reconstruction: methods based on sparsity or low-rank models, and data-driven methods based on machine learning techniques.

Computed Tomography (CT) Image Reconstruction

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

no code implementations1 Jan 2019 Zhipeng Li, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler

Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.

Learning Multi-Layer Transform Models

no code implementations19 Oct 2018 Saiprasad Ravishankar, Brendt Wohlberg

Learned data models based on sparsity are widely used in signal processing and imaging applications.

Image Denoising

Online Adaptive Image Reconstruction (OnAIR) Using Dictionary Models

no code implementations6 Sep 2018 Brian E. Moore, Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler

Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.

Denoising Image Reconstruction +1

SPULTRA: Low-Dose CT Image Reconstruction with Joint Statistical and Learned Image Models

1 code implementation27 Aug 2018 Siqi Ye, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler

The SPULTRA algorithm has a similar computational cost per iteration as its recent counterpart PWLS-ULTRA that uses post-log measurements, and it provides better image reconstruction quality than PWLS-ULTRA, especially in low-dose scans.

Signal Processing Image and Video Processing Optimization and Control Medical Physics

Analysis of Fast Structured Dictionary Learning

no code implementations31 May 2018 Saiprasad Ravishankar, Anna Ma, Deanna Needell

Sparsity-based models and techniques have been exploited in many signal processing and imaging applications.

Dictionary Learning Operator learning

VIDOSAT: High-dimensional Sparsifying Transform Learning for Online Video Denoising

1 code implementation3 Oct 2017 Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.

Dictionary Learning Image Denoising +3

Low Dose CT Image Reconstruction With Learned Sparsifying Transform

no code implementations10 Jul 2017 Xuehang Zheng, Zening Lu, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler

A major challenge in computed tomography (CT) is to reduce X-ray dose to a low or even ultra-low level while maintaining the high quality of reconstructed images.

Computed Tomography (CT) Image Reconstruction

PWLS-ULTRA: An Efficient Clustering and Learning-Based Approach for Low-Dose 3D CT Image Reconstruction

1 code implementation27 Mar 2017 Xuehang Zheng, Saiprasad Ravishankar, Yong Long, Jeffrey A. Fessler

PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.

Computed Tomography (CT) Image Reconstruction

Low-rank and Adaptive Sparse Signal (LASSI) Models for Highly Accelerated Dynamic Imaging

no code implementations13 Nov 2016 Saiprasad Ravishankar, Brian E. Moore, Raj Rao Nadakuditi, Jeffrey A. Fessler

For example, the patches of the underlying data are modeled as sparse in an adaptive dictionary domain, and the resulting image and dictionary estimation from undersampled measurements is called dictionary-blind compressed sensing, or the dynamic image sequence is modeled as a sum of low-rank and sparse (in some transform domain) components (L+S model) that are estimated from limited measurements.

Image Reconstruction

Efficient Sum of Outer Products Dictionary Learning (SOUP-DIL) and Its Application to Inverse Problems

1 code implementation19 Nov 2015 Saiprasad Ravishankar, Raj Rao Nadakuditi, Jeffrey A. Fessler

This paper exploits the ideas that drive algorithms such as K-SVD, and investigates in detail efficient methods for aggregate sparsity penalized dictionary learning by first approximating the data with a sum of sparse rank-one matrices (outer products) and then using a block coordinate descent approach to estimate the unknowns.

Denoising Dictionary Learning +1

FRIST - Flipping and Rotation Invariant Sparsifying Transform Learning and Applications

no code implementations19 Nov 2015 Bihan Wen, Saiprasad Ravishankar, Yoram Bresler

Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision.

Denoising Dictionary Learning +1

Data-Driven Learning of a Union of Sparsifying Transforms Model for Blind Compressed Sensing

no code implementations4 Nov 2015 Saiprasad Ravishankar, Yoram Bresler

In this work, we focus on blind compressed sensing (BCS), where the underlying sparse signal model is a priori unknown, and propose a framework to simultaneously reconstruct the underlying image as well as the unknown model from highly undersampled measurements.

Image Reconstruction

$\ell_0$ Sparsifying Transform Learning with Efficient Optimal Updates and Convergence Guarantees

no code implementations13 Jan 2015 Saiprasad Ravishankar, Yoram Bresler

Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary.

Image Denoising Image Reconstruction

Efficient Blind Compressed Sensing Using Sparsifying Transforms with Convergence Guarantees and Application to MRI

no code implementations13 Jan 2015 Saiprasad Ravishankar, Yoram Bresler

Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT.

Image Reconstruction

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