J
Jinli Suo
Researcher at Tsinghua University
Publications - 152
Citations - 3494
Jinli Suo is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Pixel. The author has an hindex of 28, co-authored 124 publications receiving 2587 citations. Previous affiliations of Jinli Suo include Chinese Academy of Sciences & MediaTech Institute.
Papers
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Proceedings ArticleDOI
Optical Computing System for Fast Non-uniform Image Deblurring
TL;DR: A prototype system which can fast compute non-uniform convolution for the blurring image of planar scene caused by 3D rotation and incorporate it into an iterative deblurring framework is developed.
Journal ArticleDOI
Special Section on Advanced Displays: Non-uniform image deblurring using an optical computing system
Tao Yue,Jinli Suo,Qionghai Dai +2 more
TL;DR: An efficient optical computation deblurring framework that implements the time-consuming and repeatedly required modules, i.e., non-uniform convolution and perspective warping, by light transportation is proposed and has a high generalizability to more complex camera motions.
Posted Content
Fast and High Quality Highlight Removal from A Single Image
TL;DR: Zhang et al. as discussed by the authors derived a normalized dichromatic model for the pixels with identical diffuse color: a unit circle equation of projection coefficients in two subspaces that are orthogonal to and parallel with the illumination, respectively.
Proceedings Article
Universal and Flexible Optical Aberration Correction Using Deep-Prior Based Deconvolution
TL;DR: Zhang et al. as mentioned in this paper proposed a plug-and-play deep network that takes the aberrant image and PSF map as input and produces the latent high quality version via incorporating lens-specific deep priors, thus leading to a universal and flexible optical aberration correction method.
Posted Content
Plug-and-Play Algorithms for Large-scale Snapshot Compressive Imaging
TL;DR: Wang et al. as mentioned in this paper developed fast and flexible algorithms for Snapshot Compressive Imaging (SCI) based on the plug-and-play (PnP) framework, and further proposed the PnP-GAP (generalized alternating projection) algorithm with a lower computational workload.