328 citations · 670 across the 15 of their papers we have counts for
13 papers · 1 filter
UniViTAR: Unified Vision Transformer with Native Resolution
Limeng Qiao, Yiyang Gan, Bairui Wang +4
Conventional Vision Transformer simplifies visual modeling by standardizing input resolutions, often disregarding the variability of natural visual data and compromising spatial-co…
Retinex-MEF: Retinex-based Glare Effects Aware Unsupervised Multi-Exposure Image Fusion
Haowen Bai, Jiangshe Zhang, Zixiang Zhao +3
Multi-exposure image fusion (MEF) synthesizes multiple, differently exposed images of the same scene into a single, well-exposed composite. Retinex theory, which separates image il…
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
Haowen Bai, Zixiang Zhao, Jiangshe Zhang +5
Image fusion aims to combine information from multiple source images into a single one with more comprehensive informational content. Deep learning-based image fusion algorithms fa…
Neural Gradient Regularizer
Shuang Xu, Yifan Wang, Zixiang Zhao +6
Owing to its significant success, the prior imposed on gradient maps has consistently been a subject of great interest in the field of image processing. Total variation (TV), one o…
HIDFlowNet: A Flow-Based Deep Network for Hyperspectral Image Denoising
Qizhou Wang, Li Pang, Xiangyong Cao +2
Hyperspectral image (HSI) denoising is essentially ill-posed since a noisy HSI can be degraded from multiple clean HSIs. However, existing deep learning (DL)-based approaches only…
Equivariant Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +6
Multi-modality image fusion is a technique that combines information from different sensors or modalities, enabling the fused image to retain complementary features from each modal…
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Xiang Gu +5
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal image processing, aims to upsample low-resolution (LR) depth maps with additional information involved in hi…
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
Zixiang Zhao, Haowen Bai, Jiangshe Zhang +5
Multi-modality (MM) image fusion aims to render fused images that maintain the merits of different modalities, e.g., functional highlight and detailed textures. To tackle the chall…
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
Zixiang Zhao, Jiangshe Zhang, Shuang Xu +2
Guided depth super-resolution (GDSR) is an essential topic in multi-modal image processing, which reconstructs high-resolution (HR) depth maps from low-resolution ones collected wi…
DUT-LFSaliency: Versatile Dataset and Light Field-to-RGB Saliency Detection
Yongri Piao, Zhengkun Rong, Shuang Xu +2
Light field data exhibit favorable characteristics conducive to saliency detection. The success of learning-based light field saliency detection is heavily dependent on how a compr…
Bayesian Fusion for Infrared and Visible Images
Zixiang Zhao, Shuang Xu, Chunxia Zhang +2
Infrared and visible image fusion has been a hot issue in image fusion. In this task, a fused image containing both the gradient and detailed texture information of visible images…
Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling
Zixiang Zhao, Shuang Xu, Jiangshe Zhang +3
Infrared and visible image fusion (IVIF) expects to obtain images that retain thermal radiation information from infrared images and texture details from visible images. In this pa…
MFFW: A new dataset for multi-focus image fusion
Shuang Xu, Xiaoli Wei, Chunxia Zhang +2
Multi-focus image fusion (MFF) is a fundamental task in the field of computational photography. Current methods have achieved significant performance improvement. It is found that…