Search Results for author: Man Zhou

Found 35 papers, 14 papers with code

Shuffle Mamba: State Space Models with Random Shuffle for Multi-Modal Image Fusion

no code implementations3 Sep 2024 Ke Cao, Xuanhua He, Tao Hu, Chengjun Xie, Jie Zhang, Man Zhou, Danfeng Hong

Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images.

Long-range modeling Mamba +1

Training-Free Large Model Priors for Multiple-in-One Image Restoration

no code implementations18 Jul 2024 Xuanhua He, Lang Li, Yingying Wang, Hui Zheng, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou

To address this issue, we propose Large Model Driven Image Restoration framework (LMDIR), a novel multiple-in-one image restoration paradigm that leverages the generic priors from large multi-modal language models (MMLMs) and the pretrained diffusion models.

Image Restoration

Linearly-evolved Transformer for Pan-sharpening

1 code implementation19 Apr 2024 JunMing Hou, ZiHan Cao, Naishan Zheng, Xuan Li, Xiaoyu Chen, Xinyang Liu, Xiaofeng Cong, Man Zhou, Danfeng Hong

In this way, our proposed method is capable of benefiting the cascaded modeling rule while achieving favorable performance in the efficient manner.

Pan-Mamba: Effective pan-sharpening with State Space Model

1 code implementation19 Feb 2024 Xuanhua He, Ke Cao, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou

To the best of our knowledge, this work is the first attempt in exploring the potential of the Mamba model and establishes a new frontier in the pan-sharpening techniques.

Mamba Pansharpening

Frequency-Adaptive Pan-Sharpening with Mixture of Experts

1 code implementation4 Jan 2024 Xuanhua He, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou

Pan-sharpening involves reconstructing missing high-frequency information in multi-spectral images with low spatial resolution, using a higher-resolution panchromatic image as guidance.

Enhancing RAW-to-sRGB with Decoupled Style Structure in Fourier Domain

1 code implementation4 Jan 2024 Xuanhua He, Tao Hu, Guoli Wang, Zejin Wang, Run Wang, Qian Zhang, Keyu Yan, Ziyi Chen, Rui Li, Chenjun Xie, Jie Zhang, Man Zhou

However, current methods often ignore the difference between cell phone RAW images and DSLR camera RGB images, a difference that goes beyond the color matrix and extends to spatial structure due to resolution variations.

Image Restoration

Empowering Resampling Operation for Ultra-High-Definition Image Enhancement with Model-Aware Guidance

1 code implementation CVPR 2024 Wei Yu, Jie Huang, Bing Li, Kaiwen Zheng, Qi Zhu, Man Zhou, Feng Zhao

At the second stage the image-wise compensatory information is derived with the compensatory kernels and embedded into the rescaled input images.

Image Enhancement

Probing Synergistic High-Order Interaction in Infrared and Visible Image Fusion

1 code implementation CVPR 2024 Naishan Zheng, Man Zhou, Jie Huang, JunMing Hou, Haoying Li, Yuan Xu, Feng Zhao

To bridge this gap we introduce a Synergistic High-order Interaction Paradigm (SHIP) designed to systematically investigate spatial fine-grained and global statistics collaborations between infrared and visible images across two fundamental dimensions: 1) Spatial dimension: we construct spatial fine-grained interactions through element-wise multiplication mathematically equivalent to global interactions and then foster high-order formats by iteratively aggregating and evolving complementary information enhancing both efficiency and flexibility.

Infrared And Visible Image Fusion

Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness

no code implementations4 Dec 2023 Guanlin Li, Naishan Zheng, Man Zhou, Jie Zhang, Tianwei Zhang

However, these works lack analysis of adversarial information or perturbation, which cannot reveal the mystery of adversarial examples and lose proper interpretation.

Adversarial Robustness

Empowering Low-Light Image Enhancer through Customized Learnable Priors

1 code implementation ICCV 2023 Naishan Zheng, Man Zhou, Yanmeng Dong, Xiangyu Rui, Jie Huang, Chongyi Li, Feng Zhao

In this work, we propose a paradigm for low-light image enhancement that explores the potential of customized learnable priors to improve the transparency of the deep unfolding paradigm.

Low-Light Image Enhancement

Learned Image Reasoning Prior Penetrates Deep Unfolding Network for Panchromatic and Multi-Spectral Image Fusion

no code implementations ICCV 2023 Man Zhou, Jie Huang, Naishan Zheng, Chongyi Li

Such designs penetrate the image reasoning prior into deep unfolding networks while improving its interpretability and representation capability.

Generalized Lightness Adaptation with Channel Selective Normalization

1 code implementation ICCV 2023 Mingde Yao, Jie Huang, Xin Jin, Ruikang Xu, Shenglong Zhou, Man Zhou, Zhiwei Xiong

Existing methods typically work well on their trained lightness conditions but perform poorly in unknown ones due to their limited generalization ability.

Image Retouching Low-Light Image Enhancement +1

PanFlowNet: A Flow-Based Deep Network for Pan-sharpening

no code implementations ICCV 2023 Gang Yang, Xiangyong Cao, Wenzhe Xiao, Man Zhou, Aiping Liu, Xun Chen, Deyu Meng

The experimental results verify that the proposed PanFlowNet can generate various HRMS images given an LRMS image and a PAN image.

Super-Resolution

Random Weights Networks Work as Loss Prior Constraint for Image Restoration

no code implementations29 Mar 2023 Man Zhou, Naishan Zheng, Jie Huang, Xiangyu Rui, Chunle Guo, Deyu Meng, Chongyi Li, Jinwei Gu

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 belief ``Random Weights Networks can Be Acted as Loss Prior Constraint for Image Restoration''.

Image Restoration Image Super-Resolution +1

Unlocking Masked Autoencoders as Loss Function for Image and Video Restoration

no code implementations29 Mar 2023 Man Zhou, Naishan Zheng, Jie Huang, Chunle Guo, Chongyi Li

We investigate the efficacy of our belief from three perspectives: 1) from task-customized MAE to native MAE, 2) from image task to video task, and 3) from transformer structure to convolution neural network structure.

Image Denoising Image Enhancement +4

Embedding Fourier for Ultra-High-Definition Low-Light Image Enhancement

no code implementations23 Feb 2023 Chongyi Li, Chun-Le Guo, Man Zhou, Zhexin Liang, Shangchen Zhou, Ruicheng Feng, Chen Change Loy

Our approach is motivated by a few unique characteristics in the Fourier domain: 1) most luminance information concentrates on amplitudes while noise is closely related to phases, and 2) a high-resolution image and its low-resolution version share similar amplitude patterns. Through embedding Fourier into our network, the amplitude and phase of a low-light image are separately processed to avoid amplifying noise when enhancing luminance.

4k Low-Light Image Enhancement +1

Pyramid Dual Domain Injection Network for Pan-sharpening

no code implementations ICCV 2023 Xuanhua He, Keyu Yan, Rui Li, Chengjun Xie, Jie Zhang, Man Zhou

To this end, we first revisit the degradation process of pan-sharpening in Fourier space, and then devise a Pyramid Dual Domain Injection Pan-sharpening Network upon the above observation by fully exploring and exploiting the distinguished information in both the spatial and frequency domains.

Spectral Super-Resolution Super-Resolution

Learning Sample Relationship for Exposure Correction

no code implementations CVPR 2023 Jie Huang, Feng Zhao, Man Zhou, Jie Xiao, Naishan Zheng, Kaiwen Zheng, Zhiwei Xiong

Exposure correction task aims to correct the underexposure and its adverse overexposure images to the normal exposure in a single network.

Exposure Correction Task 2

Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network

no code implementations15 Oct 2022 Keyu Yan, Man Zhou, Jie Huang, Feng Zhao, Chengjun Xie, Chongyi Li, Danfeng Hong

Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images.

Deep Fourier Up-Sampling

1 code implementation11 Oct 2022 Man Zhou, Hu Yu, Jie Huang, Feng Zhao, Jinwei Gu, Chen Change Loy, Deyu Meng, Chongyi Li

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling.

Image Dehazing Image Segmentation +4

Model-Guided Multi-Contrast Deep Unfolding Network for MRI Super-resolution Reconstruction

1 code implementation15 Sep 2022 Gang Yang, Li Zhang, Man Zhou, Aiping Liu, Xun Chen, Zhiwei Xiong, Feng Wu

Interpretable neural network models are of significant interest since they enhance the trustworthiness required in clinical practice when dealing with medical images.

Super-Resolution

Source-Free Domain Adaptation for Real-world Image Dehazing

no code implementations14 Jul 2022 Hu Yu, Jie Huang, Yajing Liu, Qi Zhu, Man Zhou, Feng Zhao

Although certain Domain Adaptation (DA) dehazing methods have been presented, they inevitably require access to the source dataset to reduce the gap between the source synthetic and target real domains.

Image Dehazing Source-Free Domain Adaptation +1

Memory-augmented Deep Unfolding Network for Guided Image Super-resolution

no code implementations12 Feb 2022 Man Zhou, Keyu Yan, Jinshan Pan, Wenqi Ren, Qi Xie, Xiangyong Cao

Guided image super-resolution (GISR) aims to obtain a high-resolution (HR) target image by enhancing the spatial resolution of a low-resolution (LR) target image under the guidance of a HR image.

Image Super-Resolution

Mutual Information-Driven Pan-Sharpening

no code implementations CVPR 2022 Man Zhou, Keyu Yan, Jie Huang, Zihe Yang, Xueyang Fu, Feng Zhao

Despite the remarkable progress, existing state-of-the-art Pan-sharpening methods don't explicitly enforce the complementary information learning between two modalities of PAN and MS images.

Memory-Augmented Deep Conditional Unfolding Network for Pan-Sharpening

1 code implementation CVPR 2022 Gang Yang, Man Zhou, Keyu Yan, Aiping Liu, Xueyang Fu, Fan Wang

Pan-sharpening aims to obtain high-resolution multispectral (MS) images for remote sensing systems and deep learning-based methods have achieved remarkable success.

Denoising

Exposure Normalization and Compensation for Multiple-Exposure Correction

no code implementations CVPR 2022 Jie Huang, Yajing Liu, Xueyang Fu, Man Zhou, Yang Wang, Feng Zhao, Zhiwei Xiong

However, the procedures of correcting underexposure and overexposure to normal exposures are much different from each other, leading to large discrepancies for the network in correcting multiple exposures, thus resulting in poor performance.

Exposure Correction Image Enhancement

Unfolding Taylor's Approximations for Image Restoration

no code implementations NeurIPS 2021 Man Zhou, Zeyu Xiao, Xueyang Fu, Aiping Liu, Gang Yang, Zhiwei Xiong

Deep learning provides a new avenue for image restoration, which demands a delicate balance between fine-grained details and high-level contextualized information during recovering the latent clear image.

Image Restoration

Image De-Raining via Continual Learning

no code implementations CVPR 2021 Man Zhou, Jie Xiao, Yifan Chang, Xueyang Fu, Aiping Liu, Jinshan Pan, Zheng-Jun Zha

The proposed model is capable of achieving superior performance on both inhomogeneous and incremental datasets, and is promising for highly compact systems to gradually learn myriad regularities of the different types of rain streaks.

Continual Learning

Improving De-Raining Generalization via Neural Reorganization

no code implementations ICCV 2021 Jie Xiao, Man Zhou, Xueyang Fu, Aiping Liu, Zheng-Jun Zha

Equipped with our NR algorithm, the deep model can be trained on a list of synthetic rainy datasets by overcoming catastrophic forgetting, making it a general-version de-raining network.

Knowledge Distillation

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