no code implementations • 15 Mar 2024 • Chong Wang, Yi Yu, Lanqing Guo, Bihan Wen
This is primarily due to the unique characteristic of spatially varying illumination within shadow images.
1 code implementation • 15 Mar 2024 • Chong Wang, Lanqing Guo, YuFei Wang, Hao Cheng, Yi Yu, Bihan Wen
Starting from decomposing the original maximum-a-posteriori problem of accelerated MRI, we present a rigorous derivation of the proposed PDAC framework, which could be further unfolded into an end-to-end trainable network.
1 code implementation • 16 Feb 2024 • Lanqing Guo, Yingqing He, Haoxin Chen, Menghan Xia, Xiaodong Cun, YuFei Wang, Siyu Huang, Yong Zhang, Xintao Wang, Qifeng Chen, Ying Shan, Bihan Wen
Diffusion models have proven to be highly effective in image and video generation; however, they still face composition challenges when generating images of varying sizes due to single-scale training data.
1 code implementation • 23 Nov 2023 • YuFei Wang, Wenhan Yang, Xinyuan Chen, Yaohui Wang, Lanqing Guo, Lap-Pui Chau, Ziwei Liu, Yu Qiao, Alex C. Kot, Bihan Wen
Extensive experiments conducted on synthetic and real-world datasets demonstrate that the proposed method can achieve comparable or even superior performance compared to both previous SOTA methods and the teacher model, in just one sampling step, resulting in a remarkable up to x10 speedup for inference.
1 code implementation • ICCV 2023 • YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C. Kot, Bihan Wen
Different from a vanilla diffusion model that has to perform Gaussian denoising, with the injected physics-based exposure model, our restoration process can directly start from a noisy image instead of pure noise.
Ranked #1 on Image Denoising on Image Denoising on SID x300
1 code implementation • 21 Jun 2023 • YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex C. Kot, Bihan Wen
Besides, we propose a novel design of the context model, which can better predict the order masks of encoding/decoding based on both the sRGB image and the masks of already processed features.
no code implementations • 3 Mar 2023 • Ziwang Xu, Lanqing Guo, Shuyan Zhang, Alex C. Kot, Bihan Wen
In this work, we propose a novel unsupervised deep learning framework for the digital staining of cell images using knowledge distillation and generative adversarial networks (GANs).
1 code implementation • CVPR 2023 • YuFei Wang, Yi Yu, Wenhan Yang, Lanqing Guo, Lap-Pui Chau, Alex Kot, Bihan Wen
While raw images exhibit advantages over sRGB images (e. g., linearity and fine-grained quantization level), they are not widely used by common users due to the large storage requirements.
1 code implementation • 3 Feb 2023 • Lanqing Guo, Siyu Huang, Ding Liu, Hao Cheng, Bihan Wen
It is still challenging for the deep shadow removal model to exploit the global contextual correlation between shadow and non-shadow regions.
Ranked #1 on Shadow Removal on ISTD
no code implementations • ICCV 2023 • Lanqing Guo, Chong Wang, Wenhan Yang, YuFei Wang, Bihan Wen
Recent deep learning methods have achieved superior results in shadow removal.
1 code implementation • CVPR 2023 • Zixuan Fu, Lanqing Guo, Bihan Wen
Modeling and synthesizing real noise in the standard RGB (sRGB) domain is challenging due to the complicated noise distribution.
no code implementations • ICCV 2023 • Hao Cheng, Siyuan Yang, Joey Tianyi Zhou, Lanqing Guo, Bihan Wen
Few-shot classification aims to learn a discriminative feature representation to recognize unseen classes with few labeled support samples.
1 code implementation • CVPR 2023 • Lanqing Guo, Chong Wang, Wenhan Yang, Siyu Huang, YuFei Wang, Hanspeter Pfister, Bihan Wen
Recent deep learning methods have achieved promising results in image shadow removal.
no code implementations • 10 Jan 2022 • Lanqing Guo, Renjie Wan, Wenhan Yang, Alex Kot, Bihan Wen
Images captured in the low-light condition suffer from low visibility and various imaging artifacts, e. g., real noise.
no code implementations • 11 Nov 2021 • Lanqing Guo, Siyu Huang, Haosen Liu, Bihan Wen
One of the fundamental challenges in image restoration is denoising, where the objective is to estimate the clean image from its noisy measurements.
no code implementations • 15 Oct 2021 • Tao Bai, Jun Zhao, Lanqing Guo, Bihan Wen
Deep learning models are vulnerable to adversarial examples and make incomprehensible mistakes, which puts a threat on their real-world deployment.
1 code implementation • 13 Jul 2021 • Rongkai Zhang, Lanqing Guo, Siyu Huang, Bihan Wen
Low-light image enhancement (LLIE) is a pervasive yet challenging problem, since: 1) low-light measurements may vary due to different imaging conditions in practice; 2) images can be enlightened subjectively according to diverse preferences by each individual.
1 code implementation • 24 Jun 2020 • Lanqing Guo, Zhiyuan Zha, Saiprasad Ravishankar, Bihan Wen
Experimental results demonstrate that (1) Self-Convolution can significantly speed up most of the popular non-local image restoration algorithms, with two-fold to nine-fold faster block matching, and (2) the proposed multi-modality image restoration scheme achieves superior denoising results in both efficiency and effectiveness on RGB-NIR images.