Search Results for author:
Found 18 papers, 8 papers with code
FD-Vision Mamba for Endoscopic Exposure Correction
In endoscopic imaging, the recorded images are prone to exposure abnormalities, so maintaining high-quality images is important to assist healthcare professionals in performing decision-making.
U-shaped Vision Mamba for Single Image Dehazing
Currently, Transformer is the most popular architecture for image dehazing, but due to its large computational complexity, its ability to handle long-range dependency is limited on resource-constrained devices.
Polyp-DAM: Polyp segmentation via depth anything model
In this paper, we unfold a new perspective on polyp segmentation modeling by leveraging the Depth Anything Model (DAM) to provide depth prior to polyp segmentation models.
MixNet: Towards Effective and Efficient UHD Low-Light Image Enhancement
To capture the long-range dependency of features without introducing excessive computational complexity, we present the Global Feature Modulation Layer (GFML).
Advancements and Trends in Ultra-High-Resolution Image Processing: An Overview
Currently, to further improve visual enjoyment, Ultra-High-Definition (UHD) images are catching wide attention.
Complex Mixer for MedMNIST Classification Decathlon
With the development of the medical image field, researchers seek to develop a class of datasets to block the need for medical knowledge, such as \text{MedMNIST} (v2).
4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images
The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images from synthetic to real domains.
TabMixer: Excavating Label Distribution Learning with Small-scale Features
Unfortunately, the feature space of the label distribution dataset is affected by human factors and the inductive bias of the feature extractor causing uncertainty in the feature space.
Label distribution learning via label correlation grid
Specifically, we compute a covariance matrix for the label space in the training set to represent the relationships between labels, then model the information distribution (Gaussian distribution function) for each element in the covariance matrix to obtain an LCG.
Label Distribution Learning via Implicit Distribution Representation
Specifically, we use deep implicit representation learning to construct a label distribution matrix with Gaussian prior constraints, where each row component corresponds to the distribution estimate of each label value, and this row component is constrained by a prior Gaussian distribution to moderate the noise and uncertainty interference of the label distribution dataset.
DPFNet: A Dual-branch Dilated Network with Phase-aware Fourier Convolution for Low-light Image Enhancement
To alleviate this problem, we propose a novel module using the Fourier coefficients, which can recover high-quality texture details under the constraint of semantics in the frequency phase and supplement the spatial domain.
UHD Image Deblurring via Multi-scale Cubic-Mixer
Currently, transformer-based algorithms are making a splash in the domain of image deblurring.
Unpaired Deep Image Dehazing Using Contrastive Disentanglement Learning
Then these negative adversaries are trained end-to-end together with the backbone representation network to enhance the discriminative information and promote factor disentanglement performance by maximizing the adversarial contrastive loss.
Single UHD Image Dehazing via Interpretable Pyramid Network
Currently, most single image dehazing models cannot run an ultra-high-resolution (UHD) image with a single GPU shader in real-time.
Zero-Reference Image Restoration for Under-Display Camera of UAV
The exposed cameras of UAV can shake, shift, or even malfunction under the influence of harsh weather, while the add-on devices (Dupont lines) are very vulnerable to damage.
Ultra-High-Definition Image Dehazing via Multi-Guided Bilateral Learning
To address the problem, we propose a novel network capable of real-time dehazing of 4K images on a single GPU, which consists of three deep CNNs.
TreeBERT: A Tree-Based Pre-Trained Model for Programming Language
In this paper, we present TreeBERT, a tree-based pre-trained model for improving programming language-oriented generation tasks.
Ultra-High-Definition Image HDR Reconstruction via Collaborative Bilateral Learning
First, we propose a dual-path network to extract content and chromatic features at a reduced resolution of the low dynamic range (LDR) input.
