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20212026
most citedEmbedding Fourier for Ultra-High-Definition Low-Light Image Enhancement

44 citations · 173 across the 28 of their papers we have counts for

collaborators

28 papers

cs.CV2026

Cross-Scale Pansharpening via ScaleFormer and the PanScale Benchmark

Ke Cao, Xuanhua He, Xueheng Li +7

Pansharpening aims to generate high-resolution multi-spectral images by fusing the spatial detail of panchromatic images with the spectral richness of low-resolution MS data. Howev…

cs.CV2026

LookWise: Knowing When and Where to Look for Fine-Grained Visual Reasoning in Multimodal Large Language Models

Yuxiang Shen, Hailong Huang, Zhenkun Gao +6

Multimodal Large Language Models (MLLMs) are shifting towards "Thinking with Images" by actively exploring image details. While effective, large-scale training is computationally e…

cs.CV2025

Distilling Textual Priors from LLM to Efficient Image Fusion

Ran Zhang, Xuanhua He, Ke Cao +4

Multi-modality image fusion aims to synthesize a single, comprehensive image from multiple source inputs. Traditional approaches, such as CNNs and GANs, offer efficiency but strugg…

cs.CV2024★ 1 cited

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

Ke Cao, Xuanhua He, Tao Hu +3

Multi-modal image fusion integrates complementary information from different modalities to produce enhanced and informative images. Although State-Space Models, such as Mamba, are…

cs.CV2024

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

Xuanhua He, Lang Li, Yingying Wang +7

Image restoration aims to reconstruct the latent clear images from their degraded versions. Despite the notable achievement, existing methods predominantly focus on handling specif…

cs.CV2024

Linearly-evolved Transformer for Pan-sharpening

Junming Hou, Zihan Cao, Naishan Zheng +6

Vision transformer family has dominated the satellite pan-sharpening field driven by the global-wise spatial information modeling mechanism from the core self-attention ingredient.…

cs.CV2024★ 6 cited

ID-Animator: Zero-Shot Identity-Preserving Human Video Generation

Xuanhua He, Quande Liu, Shengju Qian +5

Generating high-fidelity human video with specified identities has attracted significant attention in the content generation community. However, existing techniques struggle to str…

cs.CV2024★ 3 cited

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

Xuanhua He, Ke Cao, Keyu Yan +4

Pan-sharpening involves integrating information from low-resolution multi-spectral and high-resolution panchromatic images to generate high-resolution multi-spectral counterparts.…

cs.CV2024★ 1 cited

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

Xuanhua He, Tao Hu, Guoli Wang +9

RAW to sRGB mapping, which aims to convert RAW images from smartphones into RGB form equivalent to that of Digital Single-Lens Reflex (DSLR) cameras, has become an important area o…

cs.CV2024★ 3 cited

Frequency-Adaptive Pan-Sharpening with Mixture of Experts

Xuanhua He, Keyu Yan, Rui Li +3

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

cs.CV2023

Singular Regularization with Information Bottleneck Improves Model's Adversarial Robustness

Guanlin Li, Naishan Zheng, Man Zhou +2

Adversarial examples are one of the most severe threats to deep learning models. Numerous works have been proposed to study and defend adversarial examples. However, these works la…

cs.CV2023

Empowering Low-Light Image Enhancer through Customized Learnable Priors

Naishan Zheng, Man Zhou, Yanmeng Dong +4

Deep neural networks have achieved remarkable progress in enhancing low-light images by improving their brightness and eliminating noise. However, most existing methods construct e…

cs.CV2023★ 1 cited

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

Man Zhou, Jie Huang, Naishan Zheng +1

The success of deep neural networks for pan-sharpening is commonly in a form of black box, lacking transparency and interpretability. To alleviate this issue, we propose a novel mo…

cs.CV2023★ 2 cited

Generalized Lightness Adaptation with Channel Selective Normalization

Mingde Yao, Jie Huang, Xin Jin +4

Lightness adaptation is vital to the success of image processing to avoid unexpected visual deterioration, which covers multiple aspects, e.g., low-light image enhancement, image r…

cs.CV2023★ 15 cited

High-quality Image Dehazing with Diffusion Model

Hu Yu, Jie Huang, Kaiwen Zheng +1

Image dehazing is quite challenging in dense-haze scenarios, where quite less original information remains in the hazy image. Though previous methods have made marvelous progress,…

cs.CV2023★ 1 cited

Decomposition Ascribed Synergistic Learning for Unified Image Restoration

Jinghao Zhang, Feng Zhao

Learning to restore multiple image degradations within a single model is quite beneficial for real-world applications. Nevertheless, existing works typically concentrate on regardi…

cs.CV2023★ 2 cited

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

Gang Yang, Xiangyong Cao, Wenzhe Xiao +4

Pan-sharpening aims to generate a high-resolution multispectral (HRMS) image by integrating the spectral information of a low-resolution multispectral (LRMS) image with the texture…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 1 cited

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…

cs.CV2023★ 2 cited

Probability-based Global Cross-modal Upsampling for Pansharpening

Zeyu Zhu, Xiangyong Cao, Man Zhou +2

Pansharpening is an essential preprocessing step for remote sensing image processing. Although deep learning (DL) approaches performed well on this task, current upsampling methods…

cs.CV2023★ 44 cited

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…

cs.CV2022★ 8 cited

Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network

Keyu Yan, Man Zhou, Jie Huang +4

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 g…

cs.CV2022★ 28 cited

Deep Fourier Up-Sampling

Man Zhou, Hu Yu, Jie Huang +5

Existing convolutional neural networks widely adopt spatial down-/up-sampling for multi-scale modeling. However, spatial up-sampling operators (\emph{e.g.}, interpolation, transpos…

eess.IV2022★ 31 cited

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

Gang Yang, Li Zhang, Man Zhou +4

Magnetic resonance imaging (MRI) with high resolution (HR) provides more detailed information for accurate diagnosis and quantitative image analysis. Despite the significant advanc…