1k citations · 2k across the 32 of their papers we have counts for
11 papers · 1 filter
LAMP: Learn A Motion Pattern for Few-Shot-Based Video Generation
Ruiqi Wu, Liangyu Chen, Tong Yang +3
With the impressive progress in diffusion-based text-to-image generation, extending such powerful generative ability to text-to-video raises enormous attention. Existing methods ei…
Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement
Dehuan Zhang, Jingchun Zhou, ChunLe Guo +2
Visually restoring underwater scenes primarily involves mitigating interference from underwater media. Existing methods ignore the inherent scale-related characteristics in underwa…
AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection
Jingchun Zhou, Zongxin He, Kin-Man Lam +4
In this paper, we present a novel Amplitude-Modulated Stochastic Perturbation and Vortex Convolutional Network, AMSP-UOD, designed for underwater object detection. AMSP-UOD specifi…
Make Explicit Calibration Implicit: Calibrate Denoiser Instead of the Noise Model
Xin Jin, Jia-Wen Xiao, Ling-Hao Han +4
Explicit calibration-based methods have dominated RAW image denoising under extremely low-light environments. However, these methods are impeded by several critical limitations: a)…
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
Runmin Cong, Wenyu Yang, Wei Zhang +4
Due to the light absorption and scattering induced by the water medium, underwater images usually suffer from some degradation problems, such as low contrast, color distortion, and…
AMT: All-Pairs Multi-Field Transforms for Efficient Frame Interpolation
Zhen Li, Zuo-Liang Zhu, Ling-Hao Han +3
We present All-Pairs Multi-Field Transforms (AMT), a new network architecture for video frame interpolation. It is based on two essential designs. First, we build bidirectional cor…
RIDCP: Revitalizing Real Image Dehazing via High-Quality Codebook Priors
Rui-Qi Wu, Zheng-Peng Duan, Chun-Le Guo +2
Existing dehazing approaches struggle to process real-world hazy images owing to the lack of paired real data and robust priors. In this work, we present a new paradigm for real im…
Random Weights Networks Work as Loss Prior Constraint for Image Restoration
Man Zhou, Naishan Zheng, Jie Huang +5
In this paper, orthogonal to the existing data and model studies, we instead resort our efforts to investigate the potential of loss function in a new perspective and present our b…
Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration
Man Zhou, Naishan Zheng, Jie Huang +2
Image and video restoration has achieved a remarkable leap with the advent of deep learning. The success of deep learning paradigm lies in three key components: data, model, and lo…
SRFormerV2: Taking a Closer Look at Permuted Self-Attention for Image Super-Resolution
Yupeng Zhou, Zhen Li, Chun-Le Guo +3
Previous works have shown that increasing the window size for Transformer-based image super-resolution models (e.g., SwinIR) can significantly improve the model performance. Still,…
Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement
Chongyi Li, Chun-Le Guo, Man Zhou +4
Ultra-High-Definition (UHD) photo has gradually become the standard configuration in advanced imaging devices. The new standard unveils many issues in existing approaches for low-l…