no code implementations • 3 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.
no code implementations • 18 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.
1 code implementation • 19 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.
1 code implementation • 19 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.
1 code implementation • 4 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.
1 code implementation • 4 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.
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.
1 code implementation • CVPR 2024 • Jiangtong Tan, Jie Huang, Naishan Zheng, Man Zhou, Keyu Yan, Danfeng Hong, Feng Zhao
Our method extend a new space for exploring the relationships of PAN and LRMS images enhancing the integration of spatial-frequency information.
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.
no code implementations • 4 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.
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.
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.
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.
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.
no code implementations • 29 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''.
no code implementations • 29 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.
1 code implementation • CVPR 2023 • Zeyu Zhu, Xiangyong Cao, Man Zhou, Junhao Huang, Deyu Meng
Pansharpening is an essential preprocessing step for remote sensing image processing.
no code implementations • 23 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.
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.
no code implementations • CVPR 2023 • Zizheng Yang, Jie Huang, Jiahao Chang, Man Zhou, Hu Yu, Jinghao Zhang, Feng Zhao
Deep image recognition models suffer a significant performance drop when applied to low-quality images since they are trained on high-quality images.
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.
no code implementations • ICCV 2023 • Qi Zhu, Man Zhou, Naishan Zheng, Chongyi Li, Jie Huang, Feng Zhao
Video deblurring aims to restore the latent video frames from their blurred counterparts.
1 code implementation • CVPR 2023 • Jinghao Zhang, Jie Huang, Mingde Yao, Zizheng Yang, Hu Yu, Man Zhou, Feng Zhao
Learning to leverage the relationship among diverse image restoration tasks is quite beneficial for unraveling the intrinsic ingredients behind the degradation.
no code implementations • 15 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.
1 code implementation • 11 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.
1 code implementation • 15 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.
no code implementations • 15 Jul 2022 • Naishan Zheng, Jie Huang, Qi Zhu, Man Zhou, Feng Zhao, Zheng-Jun Zha
Low-light image enhancement is an inherently subjective process whose targets vary with the user's aesthetic.
no code implementations • 14 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.
no code implementations • 12 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.
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.
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.
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.
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.
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.
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.