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Jianfeng Lu

4 papers hereh-index 181.1k citations49 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CV2
  • eess.IV2
same name
  • Jianfeng Lu — 146 papers, h 35
  • Jianfeng Lu — 35 papers, h 28
  • Jianfeng Lu — 18 papers, h 5
  • Jianfeng Lu — 10 papers, h 3
  • Jianfeng Lu — 9 papers, h 7
  • Jianfeng Lu — 9 papers, h 25

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

activity
20202026
most citedHyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer

28 citations · 28 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CV2026

HyperVision: A Channel-Adaptive Ground-Based Hyperspectral Vision Pre-trained Backbone

Guanyiman Fu, Jingtao Li, Zihang Cheng +8

While hyperspectral imaging provides rich spatial-spectral information across hundreds of narrow wavelength bands for precise material identification, ground-based hyperspectral pr…

eess.IV2024★ 28 cited

Hyperspectral Image Denoising via Spatial-Spectral Recurrent Transformer

Guanyiman Fu, Fengchao Xiong, Jianfeng Lu +3

Hyperspectral images (HSIs) often suffer from noise arising from both intra-imaging mechanisms and environmental factors. Leveraging domain knowledge specific to HSIs, such as glob…

cs.CV2023

Benchmarking Ultra-High-Definition Image Reflection Removal

Zhenyuan Zhang, Zhenbo Song, Kaihao Zhang +2

Deep learning based methods have achieved significant success in the task of single image reflection removal (SIRR). However, the majority of these methods are focused on High-Defi…

eess.IV2020

SMDS-Net: Model Guided Spectral-Spatial Network for Hyperspectral Image Denoising

Fengchao Xiong, Shuyin Tao, Jun Zhou +3

Deep learning (DL) based hyperspectral images (HSIs) denoising approaches directly learn the nonlinear mapping between observed noisy images and underlying clean images. They norma…

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