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Found 24 papers, 7 papers with code
Neural Shrödinger Bridge Matching for Pansharpening
In this paper, we identify shortcomings in directly applying DPMs to the task of pansharpening as an inverse problem: 1) initiating sampling directly from Gaussian noise neglects the low-resolution multispectral image (LRMS) as a prior; 2) low sampling efficiency often necessitates a higher number of sampling steps.
SSDiff: Spatial-spectral Integrated Diffusion Model for Remote Sensing Pansharpening
Pansharpening is a significant image fusion technique that merges the spatial content and spectral characteristics of remote sensing images to generate high-resolution multispectral images.
A Novel State Space Model with Local Enhancement and State Sharing for Image Fusion
However, due to the nature of images different from casual language sequences, the limited state capacity of Mamba weakens its ability to model image information.
Content-Adaptive Non-Local Convolution for Remote Sensing Pansharpening
In this paper, we introduce a so-called content-adaptive non-local convolution (CANConv), a novel method tailored for remote sensing image pansharpening.
FusionMamba: Efficient Image Fusion with State Space Model
Image fusion aims to generate a high-resolution multi/hyper-spectral image by combining a high-resolution image with limited spectral information and a low-resolution image with abundant spectral data.
CMT: Cross Modulation Transformer with Hybrid Loss for Pansharpening
In response, we present the Cross Modulation Transformer (CMT), a pioneering method that modifies the attention mechanism.
APLA: Additional Perturbation for Latent Noise with Adversarial Training Enables Consistency
One underlying cause is that traditional diffusion models approximate Gaussian noise distribution by utilizing predictive noise, without fully accounting for the impact of inherent information within the input itself.
Gated Attention Coding for Training High-performance and Efficient Spiking Neural Networks
Experiments on CIFAR10/100 and ImageNet datasets demonstrate that GAC achieves state-of-the-art accuracy with remarkable efficiency.
Ranked #4 on
Image Classification
on CIFAR-10
(Accuracy metric)
A Theoretically Guaranteed Quaternion Weighted Schatten p-norm Minimization Method for Color Image Restoration
Very recently, a quaternion-based WNNM approach (QWNNM) has been developed to mitigate this issue, which is capable of representing the color image as a whole in the quaternion domain and preserving the inherent correlation among the three color channels.
Implicit Neural Feature Fusion Function for Multispectral and Hyperspectral Image Fusion
In this paper, inspired by previous work of MHIF task, we realize that HR-MSI could serve as a high-frequency detail auxiliary input, leading us to propose a novel INR-based hyperspectral fusion function named Implicit Neural Feature Fusion Function (INF).
DDRF: Denoising Diffusion Model for Remote Sensing Image Fusion
Denosing diffusion model, as a generative model, has received a lot of attention in the field of image generation recently, thanks to its powerful generation capability.
U2Net: A General Framework with Spatial-Spectral-Integrated Double U-Net for Image Fusion
The U2Net utilizes a spatial U-Net and a spectral U-Net to extract spatial details and spectral characteristics, which allows for the discriminative and hierarchical learning of features from diverse images.
Hyperspectral Image Super-Resolution
Image Super-Resolution
+1
TCJA-SNN: Temporal-Channel Joint Attention for Spiking Neural Networks
Given the critical role of attention mechanisms in enhancing neural network performance, the integration of SNNs and attention mechanisms exhibits potential to deliver energy-efficient and high-performance computing paradigms.
SIT: A Bionic and Non-Linear Neuron for Spiking Neural Network
Spiking Neural Networks (SNNs) have piqued researchers' interest because of their capacity to process temporal information and low power consumption.
A Triple-Double Convolutional Neural Network for Panchromatic Sharpening
Pansharpening refers to the fusion of a panchromatic image with a high spatial resolution and a multispectral image with a low spatial resolution, aiming to obtain a high spatial resolution multispectral image.
Fusformer: A Transformer-based Fusion Approach for Hyperspectral Image Super-resolution
Hyperspectral image has become increasingly crucial due to its abundant spectral information.
LAConv: Local Adaptive Convolution for Image Fusion
The convolution operation is a powerful tool for feature extraction and plays a prominent role in the field of computer vision.
Hyperspectral Image Super-Resolution
Image Super-Resolution
+1
Dynamic Cross Feature Fusion for Remote Sensing Pansharpening
In order to enhance the relationships of inter-branches, dynamic cross feature transfers are embedded into multiple branches to obtain high-resolution representations.
Hyperspectral Image Super-resolution via Deep Spatio-spectral Convolutional Neural Networks
In order to alleviate this issue, in this work, we propose a simple and efficient architecture for deep convolutional neural networks to fuse a low-resolution hyperspectral image (LR-HSI) and a high-resolution multispectral image (HR-MSI), yielding a high-resolution hyperspectral image (HR-HSI).
Rain Streak Removal for Single Image via Kernel Guided CNN
Then, our framework starts with learning the motion blur kernel, which is determined by two factors including angle and length, by a plain neural network, denoted as parameter net, from a patch of the texture component.
FastDeRain: A Novel Video Rain Streak Removal Method Using Directional Gradient Priors
In this paper, we propose a novel video rain streak removal approach FastDeRain, which fully considers the discriminative characteristics of rain streaks and the clean video in the gradient domain.
Multi-dimensional imaging data recovery via minimizing the partial sum of tubal nuclear norm
In this paper, we investigate tensor recovery problems within the tensor singular value decomposition (t-SVD) framework.
A Novel Tensor-Based Video Rain Streaks Removal Approach via Utilizing Discriminatively Intrinsic Priors
Rain streaks removal is an important issue of the outdoor vision system and has been recently investigated extensively.
Single image super-resolution by approximated Heaviside functions
We propose a new iterative model for single image super-resolution based on an observation: an image is consisted of smooth components and non-smooth components, and we use two classes of approximated Heaviside functions (AHFs) to represent them respectively.
