Search Results for author:
Found 35 papers, 11 papers with code
A PDE-Based Image Dehazing Method via Atmospheric Scattering Theory
This paper presents a novel partial differential equation (PDE) framework for single-image dehazing.
M2Restore: Mixture-of-Experts-based Mamba-CNN Fusion Framework for All-in-One Image Restoration
This targeted focus leads to more efficient and precise restoration.
UHD Image Dehazing via anDehazeFormer with Atmospheric-aware KV Cache
In this paper, we propose an efficient visual transformer framework for ultra-high-definition (UHD) image dehazing that addresses the key challenges of slow training speed and high memory consumption for existing methods.
Distribution-aware Dataset Distillation for Efficient Image Restoration
With the exponential increase in image data, training an image restoration model is laborious.
AdaQual-Diff: Diffusion-Based Image Restoration via Adaptive Quality Prompting
Restoring images afflicted by complex real-world degradations remains challenging, as conventional methods often fail to adapt to the unique mixture and severity of artifacts present.
AgentPolyp: Accurate Polyp Segmentation via Image Enhancement Agent
Since human and environmental factors interfere, captured polyp images usually suffer from issues such as dim lighting, blur, and overexposure, which pose challenges for downstream polyp segmentation tasks.
UAKNN: Label Distribution Learning via Uncertainty-Aware KNN
Label Distribution Learning (LDL) aims to characterize the polysemy of an instance by building a set of descriptive degrees corresponding to the instance.
Bridge the Gap between SNN and ANN for Image Restoration
Currently, neural networks based on the SNN (Spiking Neural Network) framework are beginning to make their mark in the field of image restoration, especially as they typically use less than 10\% of the energy of ANNs with the same architecture.
PolypFlow: Reinforcing Polyp Segmentation with Flow-Driven Dynamics
Accurate polyp segmentation remains challenging due to irregular lesion morphologies, ambiguous boundaries, and heterogeneous imaging conditions.
Multi-Exposure Image Fusion via Distilled 3D LUT Grid with Editable Mode
With the rising imaging resolution of handheld devices, existing multi-exposure image fusion algorithms struggle to generate a high dynamic range image with ultra-high resolution in real-time.
Ultra-High-Definition Dynamic Multi-Exposure Image Fusion via Infinite Pixel Learning
With the continuous improvement of device imaging resolution, the popularity of Ultra-High-Definition (UHD) images is increasing.
TSFormer: A Robust Framework for Efficient UHD Image Restoration
Ultra-high-definition (UHD) image restoration is vital for applications demanding exceptional visual fidelity, yet existing methods often face a trade-off between restoration quality and efficiency, limiting their practical deployment.
Dropout the High-rate Downsampling: A Novel Design Paradigm for UHD Image Restoration
In this paper, we propose a novel design paradigm to solve the UHD image restoration problem, called D2Net.
Accurate Forgetting for All-in-One Image Restoration Model
Our approach is a low-cost solution compared to the strategy of retraining the model from scratch, where the gradient ascent trick forgets the specified data and the performance of the adversarial sample maintenance model is robust.
Towards Ultra-High-Definition Image Deraining: A Benchmark and An Efficient Method
In this paper, we focus on the task of UHD image deraining, and contribute the first large-scale UHD image deraining dataset, 4K-Rain13k, that contains 13, 000 image pairs at 4K resolution.
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: Efficient Global Modeling for Ultra-High-Definition Image Restoration
To capture the longrange 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.
Ultra-High-Definition 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.
