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07/09/2023
Seismic Data Interpolation based on Denoising Diffusion Implicit Models with Resampling
The incompleteness of the seismic data caused by missing traces along th...
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05/23/2023
Leveraging Uncertainty Quantification for Picking Robust First Break Times
In seismic exploration, the selection of first break times is a crucial ...
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05/19/2023
Equivariant Multi-Modality Image Fusion
Multi-modality image fusion is a technique used to combine information f...
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03/15/2023
Spherical Space Feature Decomposition for Guided Depth Map Super-Resolution
Guided depth map super-resolution (GDSR), as a hot topic in multi-modal ...
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03/13/2023
DDFM: Denoising Diffusion Model for Multi-Modality Image Fusion
Multi-modality image fusion aims to combine different modalities to prod...
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02/09/2023
An information-theoretic learning model based on importance sampling
A crucial assumption underlying the most current theory of machine learn...
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02/09/2023
Importance Sampling Deterministic Annealing for Clustering
A current assumption of most clustering methods is that the training dat...
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11/26/2022
CDDFuse: Correlation-Driven Dual-Branch Feature Decomposition for Multi-Modality Image Fusion
Multi-modality (MM) image fusion aims to render fused images that mainta...
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09/07/2022
MSSPN: Automatic First Arrival Picking using Multi-Stage Segmentation Picking Network
Picking the first arrival times of prestack gathers is called First Arri...
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06/09/2022
Trajectory-dependent Generalization Bounds for Deep Neural Networks via Fractional Brownian Motion
Despite being tremendously overparameterized, it is appreciated that dee...
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05/05/2021
Understanding Long Range Memory Effects in Deep Neural Networks
Stochastic gradient descent (SGD) is of fundamental importance in deep l...
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04/14/2021
Discrete Cosine Transform Network for Guided Depth Map Super-Resolution
Guided depth super-resolution (GDSR) is a hot topic in multi-modal image...
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03/10/2021
Deep Convolutional Sparse Coding Network for Pansharpening with Guidance of Side Information
Pansharpening is a fundamental issue in remote sensing field. This paper...
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03/08/2021
Deep Gradient Projection Networks for Pan-sharpening
Pan-sharpening is an important technique for remote sensing imaging syst...
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12/31/2020
FGF-GAN: A Lightweight Generative Adversarial Network for Pansharpening via Fast Guided Filter
Pansharpening is a widely used image enhancement technique for remote se...
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12/16/2020
Domain Adaptive Object Detection via Feature Separation and Alignment
Recently, adversarial-based domain adaptive object detection (DAOD) meth...
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09/21/2020
MFIF-GAN: A New Generative Adversarial Network for Multi-Focus Image Fusion
Multi-Focus Image Fusion (MFIF) is one of the promising techniques to ob...
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09/02/2020
When Image Decomposition Meets Deep Learning: A Novel Infrared and Visible Image Fusion Method
Infrared and visible image fusion, as a hot topic in image processing an...
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05/18/2020
Deep Convolutional Sparse Coding Networks for Image Fusion
Image fusion is a significant problem in many fields including digital p...
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05/12/2020
Efficient and Interpretable Infrared and Visible Image Fusion Via Algorithm Unrolling
Infrared and visible image fusion expects to obtain images that highligh...
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05/12/2020
Bayesian Fusion for Infrared and Visible Images
Infrared and visible image fusion has been a hot issue in image fusion. ...
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02/12/2020
MFFW: A new dataset for multi-focus image fusion
Multi-focus image fusion (MFF) is a fundamental task in the field of com...
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01/22/2020
Towards A Controllable Disentanglement Network
This paper addresses two crucial problems of learning disentangled image...
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12/25/2019
Learning Controllable Disentangled Representations with Decorrelation Regularization
A crucial problem in learning disentangled image representations is cont...
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01/01/2019
Adaptive Quantile Low-Rank Matrix Factorization
Low-rank matrix factorization (LRMF) has received much popularity owing ...
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12/11/2018