Instruct: Given a scientific paper title, retrieve paper titles that are cited by the given paper.
Query: Image Restoration from Patch-based Compressed Sensing Measurement
From Denoising to Compressed Sensing
Residual Dense Network for Image Restoration
Image Restoration using Autoencoding Priors
image restoration using joint statistical modeling in a space - transform domain .
Adaptive Sampling Rate Assignment for Block Compressed Sensing ofImages Using Wavelet Transform
Prior image constrained compressed sensing (PICCS): A method to accurately reconstruct dynamic CT images from highly undersampled projection data sets
A parallel and reconfigurable architecture for efficient OMP compressive sensing reconstruction
A Nonlinear Primal-Dual Method for Total Variation-Based Image Restoration
Non-Convex Weighted Schatten p-Norm Minimization based ADMM Framework for Image Restoration.
D3: Deep Dual-Domain Based Fast Restoration of JPEG-Compressed Images
Fast & Dynamic Image Restoration using Laplace equation Based Image Inpainting
Deep Residual Learning for Compressed Sensing CT Reconstruction via Persistent Homology Analysis
Noise2Noise: Learning Image Restoration without Clean Data
Block compressed sensing of images using directional transforms
Optimal Spatial Adaptation for Patch-Based Image Denoising
Sparse Reconstruction by Separable Approximation
Modified Basis Pursuit Denoising(modified-BPDN) for noisy compressive sensing with partially known support
A fast algorithm for edgepreserving variational multichannel image restoration
Compressive Sensing
Random Filters for Compressive Sampling and Reconstruction
raji srinivasan & christine moorman strategic firm commitments and rewards for customer relationship management in online retailing .
YouthTube: Youth Video Authorship on YouTube and Vine
Positive psychological capital: beyond human and social capital
Bacteria Biotope Detection, Ontology-based Normalization, and Relation Extraction using Syntactic Rules
FA ] 2 N ov 2 00 3 An iterative thresholding algorithm for linear inverse problems with a sparsity constraint