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∇-Prox: Differentiable Proximal Algorithm Modeling for Large-Scale Optimization
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Outgoing claims: the papers this work cites, builds on, or argues with.
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Principles of Magnetic Resonance Imaging
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Global Convergence of ADMM in Nonconvex Nonsmooth Optimization
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OSQP: an operator splitting solver for quadratic programs
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Deep learning on image denoising: An overview
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Mastering the game of Go without human knowledge
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Introduction to Fourier Optics
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An iterative solution of a variational inequality for certain monotone operators in Hilbert space
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Linearized ADMM for Nonconvex Nonsmooth Optimization With Convergence Analysis
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Deep Residual Learning for Image Recognition
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End-to-End Learning for Joint Image Demosaicing, Denoising and Super-Resolution
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Multi-Stage Progressive Image Restoration
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Total Variation Optimization Layers for Computer Vision
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Restormer: Efficient Transformer for High-Resolution Image Restoration
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Deep Generalized Unfolding Networks for Image Restoration
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IMAGINE – Market-based multi-period planning of European hydrogen and natural gas infrastructure
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Plug-and-Play priors for model based reconstruction
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From learning models of natural image patches to whole image restoration
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Convex Optimization with Abstract Linear Operators
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Deep Learning Techniques for Inverse Problems in Imaging
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A Fast Alternating Direction Method for TVL1-L2 Signal Reconstruction From Partial Fourier Data
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Provable Convergence of Plug-and-Play Priors With MMSE Denoisers
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Playing with Duality: An overview of recent primal?dual approaches for solving large-scale optimization problems
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Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processing
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Plug-and-Play Methods for Integrating Physical and Learned Models in Computational Imaging: Theory, algorithms, and applications
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Depth Estimation From a Single Image Using Deep Learned Phase Coded Mask
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Deep Equilibrium Architectures for Inverse Problems in Imaging
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Sparse Representation for Color Image Restoration
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Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
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FFDNet: Toward a Fast and Flexible Solution for CNN-Based Image Denoising
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Object Detection With Deep Learning: A Review
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Anderson Acceleration for Fixed-Point Iterations
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The Primal-Dual Hybrid Gradient Method for Semiconvex Splittings
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Global Convergence of Splitting Methods for Nonconvex Composite Optimization
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Operator Splitting for a Homogeneous Embedding of the Linear Complementarity Problem
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Automatically scheduling halide image processing pipelines
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Differentiable Compound Optics and Processing Pipeline Optimization for End-to-end Camera Design
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Thallo – Scheduling for High-Performance Large-Scale Non-Linear Least-Squares Solvers
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Seeing through obstructions with diffractive cloaking
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Presolve Reductions in Mixed Integer Programming
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Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
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An Introduction to Deep Reinforcement Learning
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Benefits of a Hydrogen Network in Europe
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Automatic differentiation in machine learning: a survey
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Accelerating Quadratic Optimization with Reinforcement Learning
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Physics-based Noise Modeling for Extreme Low-light Photography
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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MSFA Image Denoising Using Physics-Based Noise Model and Noise-Decoupled Network
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Diffusion Posterior Proximal Sampling for Image Restoration
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Asynchronized Jacobi Solver on Heterogeneous Mobile Devices
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