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Songnan Li

4 papers hereh-index 191.4k citations60 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author1

Across the 4 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • cs.LG1
  • cs.MM1
same name
  • Songnan Li — 1 paper
  • Songnan Li — 1 paper, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

most citedSRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks

15 citations · 31 across the 4 of their papers we have counts for

collaborators

4 papers

cs.LG2021★ 1 cited

Efficient Micro-Structured Weight Unification and Pruning for Neural Network Compression

Sheng Lin, Wei Jiang, Wei Wang +4

Compressing Deep Neural Network (DNN) models to alleviate the storage and computation requirements is essential for practical applications, especially for resource limited devices.…

cs.CV2019★ 8 cited

End-to-End Denoising of Dark Burst Images Using Recurrent Fully Convolutional Networks

Di Zhao, Lan Ma, Songnan Li +1

When taking photos in dim-light environments, due to the small amount of light entering, the shot images are usually extremely dark, with a great deal of noise, and the color canno…

cs.CV2019★ 7 cited

MVF-Net: Multi-View 3D Face Morphable Model Regression

Fanzi Wu, Linchao Bao, Yajing Chen +5

We address the problem of recovering the 3D geometry of a human face from a set of facial images in multiple views. While recent studies have shown impressive progress in 3D Morpha…

cs.MM2019★ 15 cited

SRDGAN: learning the noise prior for Super Resolution with Dual Generative Adversarial Networks

Jingwei Guan, Cheng Pan, Songnan Li +1

Single Image Super Resolution (SISR) is the task of producing a high resolution (HR) image from a given low-resolution (LR) image. It is a well researched problem with extensive co…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.