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Found 32 papers, 15 papers with code
Seismic First Break Picking in a Higher Dimension Using Deep Graph Learning
Addressing this, we propose a novel approach using deep graph learning called DGL-FB, constructing a large graph to efficiently extract information.
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.
Stabilizing Sharpness-aware Minimization Through A Simple Renormalization Strategy
Recently, sharpness-aware minimization (SAM) has attracted a lot of attention because of its surprising effectiveness in improving generalization performance. However, training neural networks with SAM can be highly unstable since the loss does not decrease along the direction of the exact gradient at the current point, but instead follows the direction of a surrogate gradient evaluated at another point nearby.
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.
Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling
The model training is established on the denoising diffusion probabilistic model, where U-Net is equipped with the multi-head self-attention to match the noise in each step.
UPNet: Uncertainty-based Picking Deep Learning Network for Robust First Break Picking
Many deep neural networks (DNNs)-based automatic picking methods have been proposed to accelerate this processing.
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).
Information Theoretical Importance Sampling Clustering
In this paper, we propose an information theoretical importance sampling based approach for clustering problems (ITISC) which minimizes the worst case of expected distortions under the constraint of distribution deviation.
An information-theoretic learning model based on importance sampling
A crucial assumption underlying the most current theory of machine learning is that the training distribution is identical to the test distribution.
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.
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Besides, since the gather data is a set of signals which are greatly different from the natural images, it is difficult for the current method to solve the FAT picking problem in case of a low Signal to Noise Ratio (SNR).
Trajectory-dependent Generalization Bounds for Deep Neural Networks via Fractional Brownian Motion
In this study, we argue that the hypothesis set SGD explores is trajectory-dependent and thus may provide a tighter bound over its Rademacher complexity.
Understanding Short-Range Memory Effects in Deep Neural Networks
Instead, inspired by the short-range correlation emerging in the SGN series, we propose that SGD can be viewed as a discretization of an SDE driven by fractional Brownian motion (FBM).
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.
Towards Reducing Severe Defocus Spread Effects for Multi-Focus Image Fusion via an Optimization Based Strategy
According to the idea that each local region in the fused image should be similar to the sharpest one among source images, this paper presents an optimization-based approach to reduce defocus spread effects.
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
Bayesian Fusion for Infrared and Visible Images
In this paper, a novel Bayesian fusion model is established for infrared and visible images.
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
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
MFFW: A new dataset for multi-focus image fusion
It is found that current methods are evaluated on simulated image sets or Lytro dataset.
Toward a Controllable Disentanglement Network
By exploring the real-valued space of the soft target representation, we are able to synthesize novel images with the designated properties.
Learning Controllable Disentangled Representations with Decorrelation Regularization
By exploiting the real-valued space of the soft target representations, we are able to synthesize novel images with the designated properties.
Adaptive Quantile Low-Rank Matrix Factorization
By assuming noise to come from a Gaussian, Laplace or mixture of Gaussian distributions, significant efforts have been made on optimizing the (weighted) $L_1$ or $L_2$-norm loss between an observed matrix and its bilinear factorization.
Variational Bayesian Weighted Complex Network Reconstruction
Complex network reconstruction is a hot topic in many fields.
