Search Results for author: Lanqing Guo

Found 18 papers, 11 papers with code

Progressive Divide-and-Conquer via Subsampling Decomposition for Accelerated MRI

1 code implementation15 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.

MRI Reconstruction

Make a Cheap Scaling: A Self-Cascade Diffusion Model for Higher-Resolution Adaptation

1 code implementation16 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.

Video Generation

SinSR: Diffusion-Based Image Super-Resolution in a Single Step

1 code implementation23 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.

Image Super-Resolution

ExposureDiffusion: Learning to Expose for Low-light Image Enhancement

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.

Image Denoising Low-Light Image Enhancement

Beyond Learned Metadata-based Raw Image Reconstruction

1 code implementation21 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.

Image Compression Image Reconstruction +1

Unsupervised Deep Digital Staining For Microscopic Cell Images Via Knowledge Distillation

no code implementations3 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).

Colorization Knowledge Distillation +1

Raw Image Reconstruction with Learned Compact Metadata

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.

Image Compression Image Reconstruction +1

ShadowFormer: Global Context Helps Image Shadow Removal

1 code implementation3 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.

Image Shadow Removal Shadow Removal

Frequency Guidance Matters in Few-Shot Learning

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.

Few-Shot Learning Metric Learning

sRGB Real Noise Synthesizing With Neighboring Correlation-Aware Noise Model

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.

Denoising

Enhancing Low-Light Images in Real World via Cross-Image Disentanglement

no code implementations10 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.

Disentanglement Low-Light Image Enhancement

FINO: Flow-based Joint Image and Noise Model

no code implementations11 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.

Denoising Image Restoration

Adversarial Purification through Representation Disentanglement

no code implementations15 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.

Disentanglement

ReLLIE: Deep Reinforcement Learning for Customized Low-Light Image Enhancement

1 code implementation13 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.

Low-Light Image Enhancement reinforcement-learning +2

Exploiting Non-Local Priors via Self-Convolution For Highly-Efficient Image Restoration

1 code implementation24 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.

Denoising Image Reconstruction +1

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