UIE
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Underwater image enhancement is a technique used to improve the quality of underwater images. Due to the unique properties of the underwater environment, images captured underwater often suffer from degradation caused by absorption and scattering of light. These effects can result in low contrast, blurred images with a dominant blue or green color cast. Enhancement techniques aim to correct these issues and improve the visibility within the image. These methods can include color correction to remove the color cast, contrast enhancement to improve the visibility of underwater objects, and dehazing techniques to reduce the scattering effect. These enhancements are crucial in various applications, including underwater exploration, marine biology research, underwater archaeology, and in improving the performance of underwater vision systems used in autonomous underwater vehicles.
논문
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Adaptive Dual-domain Learning for Underwater Image Enhancement
Underwater Image Enhancement by Convolutional Spiking Neural Networks
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach
Crab: A Unified Audio-Visual Scene Understanding Model with Explicit Cooperation
Towards Robust Universal Information Extraction: Benchmark, Evaluation, and Solution
Label Drop for Multi-Aspect Relation Modeling in Universal Information Extraction
Color Correction Meets Cross-Spectral Refinement: A Distribution-Aware Diffusion for Underwater Image Restoration
Underwater Image Enhancement with Cascaded Contrastive Learning
Underwater Image Enhancement with Physical-based Denoising Diffusion Implicit Models
RUIE: Retrieval-based Unified Information Extraction using Large Language Model
RexUniNLU: Recursive Method with Explicit Schema Instructor for Universal NLU
SINET: Sparsity-driven Interpretable Neural Network for Underwater Image Enhancement
O-Mamba: O-shape State-Space Model for Underwater Image Enhancement
UIE-UnFold: Deep Unfolding Network with Color Priors and Vision Transformer for Underwater Image Enhancement
Mamba-UIE: Enhancing Underwater Images with Physical Model Constraint
Image-Conditional Diffusion Transformer for Underwater Image Enhancement
A Physical Model-Guided Framework for Underwater Image Enhancement and Depth Estimation
IDA-UIE: An Iterative Framework for Deep Network-based Degradation Aware Underwater Image Enhancement
PixMamba: Leveraging State Space Models in a Dual-Level Architecture for Underwater Image Enhancement
A Comprehensive Survey on Underwater Image Enhancement Based on Deep Learning
Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
Enhancing User Interest based on Stream Clustering and Memory Networks in Large-Scale Recommender Systems
WaterMamba: Visual State Space Model for Underwater Image Enhancement
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder
MambaUIE&SR: Unraveling the Ocean's Secrets with Only 2.8 GFLOPs
UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
A Real-Time Framework for Domain-Adaptive Underwater Object Detection with Image Enhancement
KnowCoder: Coding Structured Knowledge into LLMs for Universal Information Extraction
Learning A Physical-aware Diffusion Model Based on Transformer for Underwater Image Enhancement
CPDM: Content-Preserving Diffusion Model for Underwater Image Enhancement
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
DGNet: Dynamic Gradient-Guided Network for Water-Related Optics Image Enhancement
UIEDP:Underwater Image Enhancement with Diffusion Prior
Wavelet-based Fourier Information Interaction with Frequency Diffusion Adjustment for Underwater Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.