6 citations · 14 across the 13 of their papers we have counts for
14 papers
Binarized High-Efficiency RAW Video Restoration and Beyond
Tianyu Zhu, Ying Fu, Hesong Li +3
RAW video restoration is fundamental to high-quality low-level perception and serves as the basis for a wide range of downstream vision applications. While binary neural networks (…
Proximal Algorithm Unrolling: Flexible and Efficient Reconstruction Networks for Single-Pixel Imaging
Ping Wang, Lishun Wang, Gang Qu +3
Deep-unrolling and plug-and-play (PnP) approaches have become the de-facto standard solvers for single-pixel imaging (SPI) inverse problem. PnP approaches, a class of iterative alg…
Prior-guided Hierarchical Harmonization Network for Efficient Image Dehazing
Xiongfei Su, Siyuan Li, Yuning Cui +7
Image dehazing is a crucial task that involves the enhancement of degraded images to recover their sharpness and textures. While vision Transformers have exhibited impressive resul…
Dual-branch Graph Feature Learning for NLOS Imaging
Xiongfei Su, Tianyi Zhu, Lina Liu +6
The domain of non-line-of-sight (NLOS) imaging is advancing rapidly, offering the capability to reveal occluded scenes that are not directly visible. However, contemporary NLOS sys…
Detail Matters: Mamba-Inspired Joint Unfolding Network for Snapshot Spectral Compressive Imaging
Mengjie Qin, Yuchao Feng, Zongliang Wu +2
In the coded aperture snapshot spectral imaging system, Deep Unfolding Networks (DUNs) have made impressive progress in recovering 3D hyperspectral images (HSIs) from a single 2D m…
ARB-LLM: Alternating Refined Binarizations for Large Language Models
Zhiteng Li, Xianglong Yan, Tianao Zhang +7
Large Language Models (LLMs) have greatly pushed forward advancements in natural language processing, yet their high memory and computational demands hinder practical deployment. B…
Hierarchical Separable Video Transformer for Snapshot Compressive Imaging
Ping Wang, Yulun Zhang, Lishun Wang +1
Transformers have achieved the state-of-the-art performance on solving the inverse problem of Snapshot Compressive Imaging (SCI) for video, whose ill-posedness is rooted in the mix…
Binarized Diffusion Model for Image Super-Resolution
Zheng Chen, Haotong Qin, Yong Guo +4
Advanced diffusion models (DMs) perform impressively in image super-resolution (SR), but the high memory and computational costs hinder their deployment. Binarization, an ultra-com…
HDR-GS: Efficient High Dynamic Range Novel View Synthesis at 1000x Speed via Gaussian Splatting
Yuanhao Cai, Zihao Xiao, Yixun Liang +5
High dynamic range (HDR) novel view synthesis (NVS) aims to create photorealistic images from novel viewpoints using HDR imaging techniques. The rendered HDR images capture a wider…
Binarized Low-light Raw Video Enhancement
Gengchen Zhang, Yulun Zhang, Xin Yuan +1
Recently, deep neural networks have achieved excellent performance on low-light raw video enhancement. However, they often come with high computational complexity and large memory…
Radiative Gaussian Splatting for Efficient X-ray Novel View Synthesis
Yuanhao Cai, Yixun Liang, Jiahao Wang +5
X-ray is widely applied for transmission imaging due to its stronger penetration than natural light. When rendering novel view X-ray projections, existing methods mainly based on N…
DPoser: Diffusion Model as Robust 3D Human Pose Prior
Junzhe Lu, Jing Lin, Hongkun Dou +4
This work targets to construct a robust human pose prior. However, it remains a persistent challenge due to biomechanical constraints and diverse human movements. Traditional prior…
BinaryHPE: 3D Human Pose and Shape Estimation via Binarization
Zhiteng Li, Yulun Zhang, Jing Lin +5
3D human pose and shape estimation (HPE) aims to reconstruct the 3D human body, face, and hands from a single image. Although powerful deep learning models have achieved accurate e…
Image Super-Resolution with Text Prompt Diffusion
Zheng Chen, Yulun Zhang, Jinjin Gu +4
Image super-resolution (SR) methods typically model degradation to improve reconstruction accuracy in complex and unknown degradation scenarios. However, extracting degradation inf…