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most citedTowards Biosignals-Free Autonomous Prosthetic Hand Control via Imitation Learning

4 citations · 5 across the 5 of their papers we have counts for

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cs.CV2026

DocPure: Prompt-Free Unified Document Restoration via Degradation-Aware Structure-Guided Wavelet Modulation

Lingming Su, Wanglong Lu, Tao Wang +4

High-quality document images are pivotal for information archiving and downstream automatic processing. However, they are frequently compromised by diverse degradations during unco…

cs.CV2026

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing

Wanglong Lu, Lingming Su, Kaijie Shi +4

Recent diffusion-based generative models have shown impressive performance in image generation and editing. However, due to memory limitations and the high cost of collecting high-…

cs.CV20261 cited

EchoSR: Efficient Context Harnessing for Lightweight Image Super-Resolution

Hanli Zhao, Binhao Wang, Shihao Zhao +3

Image super-resolution (SR) aims to reconstruct high-quality, high-resolution (HR) images from low-resolution (LR) inputs and plays a critical role in various downstream applicatio…

cs.CV2025

PromptRR: Diffusion Models as Prompt Generators for Single Image Reflection Removal

Tao Wang, Wanglong Lu, Kaihao Zhang +2

Existing single image reflection removal (SIRR) methods using deep learning tend to miss key low-frequency (LF) and high-frequency (HF) differences in images, affecting their effec…

cs.CV2025

TextDoctor: Unified Document Image Inpainting via Patch Pyramid Diffusion Models

Wanglong Lu, Lingming Su, Jingjing Zheng +6

Digital versions of real-world text documents often suffer from issues like environmental corrosion of the original document, low-quality scanning, or human interference. Existing…

cs.CV2024

Visual Style Prompt Learning Using Diffusion Models for Blind Face Restoration

Wanglong Lu, Jikai Wang, Tao Wang +3

Blind face restoration aims to recover high-quality facial images from various unidentified sources of degradation, posing significant challenges due to the minimal information ret…