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.
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.
Furthermore, we present a unified framework for incorporating various GSR and LR models and discuss the relationship between GSR and LR models.
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.
We present a method for supervised learning of sparsity-promoting regularizers, a key ingredient in many modern signal reconstruction problems.
There is much recent interest in techniques to accelerate the data acquisition process in MRI by acquiring limited measurements.
Object density reconstruction from projections containing scattered radiation and noise is of critical importance in many applications.
In this paper, we compare idealized versions of these two approaches with synthetic experiments.
We also compare the proposed method to alternative approaches for combining dictionary-based methods with supervised learning in MR image reconstruction.
Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements.
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.
Achieving high-quality reconstructions from low-dose computed tomography (LDCT) measurements is of much importance in clinical settings.
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.
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.
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.
We present a method for supervised learning of sparsity-promoting regularizers for image denoising.
Signal models based on sparse representation have received considerable attention in recent years.
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.
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.
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.
The model could be pre-learned from datasets, or learned simultaneously with the reconstruction, i. e., blind CS (BCS).
Dual energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability.
Sparsity and low-rank models have been popular for reconstructing images and videos from limited or corrupted measurements.
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
Alternating minimization algorithms have been particularly popular in dictionary or transform learning.
Transform learning methods involve cheap computations and have been demonstrated to perform well in applications such as image denoising and medical image reconstruction.
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.
PWLS with regularization based on a union of learned transforms leads to better image reconstructions than using a single learned square transform.
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.
The proposed block coordinate descent algorithm involves efficient closed-form solutions.
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.
Features based on sparse representation, especially using the synthesis dictionary model, have been heavily exploited in signal processing and computer vision.
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.
Many applications in signal processing benefit from the sparsity of signals in a certain transform domain or dictionary.
Natural signals and images are well-known to be approximately sparse in transform domains such as Wavelets and DCT.