6 papers
TUDSR: Twice Upsampling-Diffusion for Higher Super-Resolution
Zhiqiang Wu, Yitong Dong, Xian Wei
Diffusion-based generative models have achieved remarkable success in real-world image super-resolution (SR). With tiled diffusion techniques, these models can produce high-resolut…
One-Shot Refiner: Boosting Feed-forward Novel View Synthesis via One-Step Diffusion
Yitong Dong, Qi Zhang, Minchao Jiang +6
We present a novel framework for high-fidelity novel view synthesis (NVS) from sparse images, addressing key limitations in recent feed-forward 3D Gaussian Splatting (3DGS) methods…
OMGSR: You Only Need One Mid-timestep Guidance for Real-World Image Super-Resolution
Zhiqiang Wu, Zhaomang Sun, Tong Zhou +7
Denoising Diffusion Probabilistic Models (DDPMs) show promising potential in one-step Real-World Image Super-Resolution (Real-ISR). Current one-step Real-ISR methods typically inje…
CIM-NET: A Video Denoising Deep Neural Network Model Optimized for Computing-in-Memory Architectures
Shan Gao, Zhiqiang Wu, Yawen Niu +2
While deep neural network (DNN)-based video denoising has demonstrated significant performance, deploying state-of-the-art models on edge devices remains challenging due to stringe…
Relaxed Rotational Equivariance via -Biases in Vision
Zhiqiang Wu, Yingjie Liu, Licheng Sun +7
Group Equivariant Convolution (GConv) can capture rotational equivariance from original data. It assumes uniform and strict rotational equivariance across all features as the trans…
R2Det: Exploring Relaxed Rotation Equivariance in 2D object detection
Zhiqiang Wu, Yingjie Liu, Hanlin Dong +5
Group Equivariant Convolution (GConv) empowers models to explore underlying symmetry in data, improving performance. However, real-world scenarios often deviate from ideal symmetri…