Search Results for author:
Found 27 papers, 11 papers with code
OID: Outlier Identifying and Discarding in Blind Image Deblurring
Even a small amount of outliers can dramatically degrade the quality of the estimated blur kernel, because the outliers are not conforming to the linear formation of the blurring process.
Enhanced Sparse Model for Blind Deblurring
Specifically, we use a weighted combination of a dense function (i. e. l2) and a newly designed enhanced sparse model termed as le, which is developed from two sparse models (i. e. l1 and l0), to fulfill the task.
LSRFormer: Efficient Transformer Supply Convolutional Neural Networks with Global Information for Aerial Image Segmentation
Convolutional Neural Networks (CNNs) can capture local context information well but cannot model the global dependencies.
Three-Stage Cascade Framework for Blurry Video Frame Interpolation
Besides, experiments on real-world blurry videos also indicate the good generalization ability of our model.
Deep Unfolding Convolutional Dictionary Model for Multi-Contrast MRI Super-resolution and Reconstruction
We test our MC-CDic model on multi-contrast MRI SR and reconstruction tasks.
DSAT-Net: Dual Spatial Attention Transformer for Building Extraction from Aerial Images
The local attention path (LAP) uses efficient stripe convolution to generate local attention, which can alleviate the loss of information caused by down-sampling operation in the GAP and supplement the spatial details.
Extracting Buildings In Remote Sensing Images
Semantic Segmentation
SDSC-UNet: Dual Skip Connection ViT-based U-shaped Model for Building Extraction
Furthermore, unlike the previous single-skip-connection structure of U-shaped methods, we build a novel dual skip connection structure inside the model.
Extracting Buildings In Remote Sensing Images
Semantic Segmentation
CP$^3$: Channel Pruning Plug-in for Point-based Networks
CP$^3$ is elaborately designed to leverage the characteristics of point clouds and PNNs in order to enable 2D channel pruning methods for PNNs.
CP3: Channel Pruning Plug-In for Point-Based Networks
Directly implementing the 2D CNN channel pruning methods to PNNs undermine the performance of PNNs because of the different representations of 2D images and 3D point clouds as well as the network architecture disparity.
Indoor Depth Recovery Based on Deep Unfolding with Non-Local Prior
Utilizing the property that there is a large amount of non-local common characteristics in depth images, we propose a novel model-guided depth recovery method, namely the DC-NLAR model.
Decomposition-Based Variational Network for Multi-Contrast MRI Super-Resolution and Reconstruction
We thus build a model to reconstruct the target image and decompose the reference image as a common component and a unique component.
Flow Guidance Deformable Compensation Network for Video Frame Interpolation
Under the guidance of the flow priors learned in step one, the deformation step designs a pyramid deformable compensation network to compensate for the missing details of the flow step.
Label-Guided Auxiliary Training Improves 3D Object Detector
Detecting 3D objects from point clouds is a practical yet challenging task that has attracted increasing attention recently.
Multiple Degradation and Reconstruction Network for Single Image Denoising via Knowledge Distillation
Single image denoising (SID) has achieved significant breakthroughs with the development of deep learning.
Hybrid Atlas Building with Deep Registration Priors
Registration-based atlas building often poses computational challenges in high-dimensional image spaces.
Contrastive Learning for Local and Global Learning MRI Reconstruction
Therefore, we propose a Spatial and Fourier Layer (SFL) to simultaneously learn the local and global information in Spatial and Fourier domains.
Structure-Preserving Deraining with Residue Channel Prior Guidance
Although these methods can remove part of the rain streaks, it is difficult for them to adapt to real-world scenarios and restore high-quality rain-free images with clear and accurate structures.
Feedback Network for Mutually Boosted Stereo Image Super-Resolution and Disparity Estimation
Besides the cross-view information exploitation in the low-resolution (LR) space, HR representations produced by the SR process are utilized to perform HR disparity estimation with higher accuracy, through which the HR features can be aggregated to generate a finer SR result.
Efficient and Accurate Multi-scale Topological Network for Single Image Dehazing
To achieve this, we propose a Multi-scale Topological Network (MSTN) to fully explore the features at different scales.
Survey of the Detection and Classification of Pulmonary Lesions via CT and X-Ray
In recent years, the prevalence of several pulmonary diseases, especially the coronavirus disease 2019 (COVID-19) pandemic, has attracted worldwide attention.
MDCN: Multi-scale Dense Cross Network for Image Super-Resolution
Among them, MDCB aims to detect multi-scale features and maximize the use of image features flow at different scales, HFDB focuses on adaptively recalibrate channel-wise feature responses to achieve feature distillation, and DRB attempts to reconstruct SR images with different upsampling factors in a single model.
Boundary Guidance Hierarchical Network for Real-Time Tongue Segmentation
Finally, the proposed hybrid loss in a four hierarchy-pixel, patch, map and boundary guides the network to effectively segment the tongue regions and accurate tongue boundaries.
Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction
Compressed Sensing MRI (CS-MRI) aims at reconstrcuting de-aliased images from sub-Nyquist sampling k-space data to accelerate MR Imaging.
Defending Against Adversarial Attacks by Suppressing the Largest Eigenvalue of Fisher Information Matrix
We propose a scheme for defending against adversarial attacks by suppressing the largest eigenvalue of the Fisher information matrix (FIM).
The Adversarial Attack and Detection under the Fisher Information Metric
By considering the data space as a non-linear space with the Fisher information metric induced from a neural network, we first propose an adversarial attack algorithm termed one-step spectral attack (OSSA).
Multi-scale Residual Network for Image Super-Resolution
Meanwhile, we let these features interact with each other to get the most efficacious image information, we call this structure Multi-scale Residual Block (MSRB).
Preselection via Classification: A Case Study on Evolutionary Multiobjective Optimization
Finally it uses the classifier to filter the unpromising candidate offspring solutions and choose a promising one from the generated candidate offspring set for each parent solution.
