Our second set of numerical results considers a simple and effective domain adaption strategy that closes the performance gap due to the use of mismatched denoisers.
Plug-and-play (PnP) prior is a well-known class of methods for solving imaging inverse problems by computing fixed-points of operators combining physical measurement models and learned image denoisers.
ELDER is based on a regularization functional parameterized by a CNN and a deep equilibrium learning (DEQ) method for training the functional to be MSE-optimal at the fixed points of the reconstruction algorithm.
Limited-Angle Computed Tomography (LACT) is a non-destructive evaluation technique used in a variety of applications ranging from security to medicine.
Deep model-based architectures (DMBAs) are widely used in imaging inverse problems to integrate physical measurement models and learned image priors.
Implicit neural representations (INR) have been recently proposed as deep learning (DL) based solutions for image compression.
Quantitative MRI (qMRI) refers to a class of MRI methods for quantifying the spatial distribution of biological tissue parameters.
Our numerical results on in-vivo MRI data show that SelfDEQ leads to state-of-the-art performance using only undersampled and noisy training data.
However, estimation of accurate CSMs is a challenging problem when measurements are highly undersampled.
Three-dimensional fluorescence microscopy often suffers from anisotropy, where the resolution along the axial direction is lower than that within the lateral imaging plane.
While the empirical performance and theoretical properties of DMBAs have been widely investigated, the existing work in the area has primarily focused on their performance when the desired image prior is known exactly.
However, the dependence of the computational/memory complexity of the measurement models in PnP/RED on the total number of measurements leaves DEQ impractical for many imaging applications.
We propose a new plug-and-play priors (PnP) based MR image reconstruction method that systematically enforces data consistency while also exploiting deep-learning priors.
Plug-and-Play Priors (PnP) is one of the most widely-used frameworks for solving computational imaging problems through the integration of physical models and learned models.
This paper considers the problem of temporal video interpolation, where the goal is to synthesize a new video frame given its two neighbors.
Ranked #1 on Video Frame Interpolation on Xiph 4k
Regularization by denoising (RED) is a widely-used framework for solving inverse problems by leveraging image denoisers as image priors.
Our new Bregman Proximal Gradient Method variant of PnP (PnP-BPGM) and Bregman Steepest Descent variant of RED (RED-BSD) replace the traditional updates in PnP and RED from the quadratic norms to more general Bregman distance.
LEARN-IMG performs motion correction on mGRE images and relies on the subsequent analysis for the estimation of $R_2^\ast$ maps, while LEARN-BIO directly performs motion- and $B0$-inhomogeneity-corrected $R_2^\ast$ estimation.
Deep neural networks for medical image reconstruction are traditionally trained using high-quality ground-truth images as training targets.
The plug-and-play priors (PnP) and regularization by denoising (RED) methods have become widely used for solving inverse problems by leveraging pre-trained deep denoisers as image priors.
We propose Coordinate-based Internal Learning (CoIL) as a new deep-learning (DL) methodology for the continuous representation of measurements.
Deep unfolding networks have recently gained popularity in the context of solving imaging inverse problems.
Cal-RED extends the traditional RED methodology to imaging problems that require the calibration of the measurement operator.
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors.
One of the key limitations in conventional deep learning based image reconstruction is the need for registered pairs of training images containing a set of high-quality groundtruth images.
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers.
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser.
We introduce a new algorithm for regularized reconstruction of multispectral (MS) images from noisy linear measurements.
In this work, we develop a new block coordinate RED algorithm that decomposes a large-scale estimation problem into a sequence of updates over a small subset of the unknown variables.
The plug-and-play priors (PnP) framework has been recently shown to achieve state-of-the-art results in regularized image reconstruction by leveraging a sophisticated denoiser within an iterative algorithm.
In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction.
The results in this paper have the potential to expand the applicability of the PnP framework to very large and redundant datasets.
Stochastic gradient descent (SGD) is one of the most widely used optimization methods for parallel and distributed processing of large datasets.
The problem of image reconstruction under multiple light scattering is usually formulated as a regularized non-convex optimization.
Common techniques that attempt to resolve the antenna ambiguity generally assume an unknown gain and phase error afflicting the radar measurements.
Image reconstruction under multiple light scattering is crucial in a number of applications such as diffraction tomography.
In the past decade, sparsity-driven regularization has led to advancement of image reconstruction algorithms.
The problem of reconstructing an object from the measurements of the light it scatters is common in numerous imaging applications.
Computational imaging methods that can exploit multiple modalities have the potential to enhance the capabilities of traditional sensing systems.
Specifically, it corresponds to a series expansion of the scattered wave with an accelerated-gradient method.
We propose a new compressive imaging method for reconstructing 2D or 3D objects from their scattered wave-field measurements.
The Iterative Born Approximation (IBA) is a well-known method for describing waves scattered by semi-transparent objects.