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Found 20 papers, 11 papers with code
BinaryDM: Towards Accurate Binarization of Diffusion Model
With the advancement of diffusion models (DMs) and the substantially increased computational requirements, quantization emerges as a practical solution to obtain compact and efficient low-bit DMs.
Make Continual Learning Stronger via C-Flat
A general framework of C-Flat applied to all CL categories and a thorough comparison with loss minima optimizer and flat minima based CL approaches is presented in this paper, showing that our method can boost CL performance in almost all cases.
Image Fusion via Vision-Language Model
Therefore, we introduce a novel fusion paradigm named image Fusion via vIsion-Language Model (FILM), for the first time, utilizing explicit textual information in different source images to guide image fusion.
ReFusion: Learning Image Fusion from Reconstruction with Learnable Loss via Meta-Learning
To ensure the fusion module maximally preserves the information from the source images, enabling the reconstruction of the source images from the fused image, we adopt a meta-learning strategy to train the loss proposal module using reconstruction loss.
Neural Gradient Regularizer
NGR is applicable to various image types and different image processing tasks, functioning in a zero-shot learning fashion, making it a versatile and plug-and-play regularizer.
Equivariant Multi-Modality Image Fusion
These components enable the net training to follow the principles of the natural sensing-imaging process while satisfying the equivariant imaging prior.
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Then, the extracted features are mapped to the spherical space to complete the separation of private features and the alignment of shared features.
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
To leverage strong generative priors and address challenges such as unstable training and lack of interpretability for GAN-based generative methods, we propose a novel fusion algorithm based on the denoising diffusion probabilistic model (DDPM).
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
We then introduce a dual-branch Transformer-CNN feature extractor with Lite Transformer (LT) blocks leveraging long-range attention to handle low-frequency global features and Invertible Neural Networks (INN) blocks focusing on extracting high-frequency local information.
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
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 with suboptimal conditions with the help of HR RGB images of the same scene.
Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
Pansharpening is a fundamental issue in remote sensing field.
Deep Gradient Projection Networks for Pan-sharpening
Specifically, two optimization problems regularized by the deep prior are formulated, and they are separately responsible for the generative models for panchromatic images and low resolution multispectral images.
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter
In addition, the latent information of features can be preserved effectively through adversarial training.
Domain Adaptive Object Detection via Feature Separation and Alignment
Notably, scale-space filtering is exploited to implement adaptive searching for regions to be aligned, and instance-level features in each region are refined to reduce redundancy and noise mentioned in the second issue.
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Multi-Focus Image Fusion (MFIF) is a promising image enhancement technique to obtain all-in-focus images meeting visual needs and it is a precondition of other computer vision tasks.
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method
The core idea is that the encoder decomposes an image into base and detail feature maps with low- and high-frequency information, respectively, and that the decoder is responsible for the original image reconstruction.
Deep Convolutional Sparse Coding Networks for Image Fusion
Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few.
Infrared And Visible Image Fusion
Multi-Exposure Image Fusion
Efficient and Model-Based Infrared and Visible Image Fusion Via Algorithm Unrolling
The proposed AUIF model starts with the iterative formulas of two traditional optimization models, which are established to accomplish two-scale decomposition, i. e., separating low-frequency base information and high-frequency detail information from source images.
Infrared And Visible Image Fusion
Rolling Shutter Correction
Bayesian Fusion for Infrared and Visible Images
In this paper, a novel Bayesian fusion model is established for infrared and visible images.
DIDFuse: Deep Image Decomposition for Infrared and Visible Image Fusion
Infrared and visible image fusion, a hot topic in the field of image processing, aims at obtaining fused images keeping the advantages of source images.
Ranked #5 on
Semantic Segmentation
on FMB Dataset
