301 citations · 1.4k across the 51 of their papers we have counts for
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Joint Reconstruction and Calibration using Regularization by Denoising
Mingyang Xie, Yu Sun, Jiaming Liu +2
Regularization by denoising (RED) is a broadly applicable framework for solving inverse problems by using priors specified as denoisers. While RED has been shown to provide state-o…
Async-RED: A Provably Convergent Asynchronous Block Parallel Stochastic Method using Deep Denoising Priors
Yu Sun, Jiaming Liu, Yiran Sun +2
Regularization by denoising (RED) is a recently developed framework for solving inverse problems by integrating advanced denoisers as image priors. Recent work has shown its state-…
Physics-Consistent Data-driven Waveform Inversion with Adaptive Data Augmentation
Renán Rojas-Gómez, Jihyun Yang, Youzuo Lin +2
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI probl…
Scalable Plug-and-Play ADMM with Convergence Guarantees
Yu Sun, Zihui Wu, Xiaojian Xu +2
Plug-and-play priors (PnP) is a broadly applicable methodology for solving inverse problems by exploiting statistical priors specified as denoisers. Recent work has reported the st…
Provable Convergence of Plug-and-Play Priors with MMSE denoisers
Xiaojian Xu, Yu Sun, Jiaming Liu +2
Plug-and-play priors (PnP) is a methodology for regularized image reconstruction that specifies the prior through an image denoiser. While PnP algorithms are well understood for de…
Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
Manish Bhattarai, Diane Oyen, Juan Castorena +2
Resolution of the complex problem of image retrieval for diagram images has yet to be reached. Deep learning methods continue to excel in the fields of object detection and image c…