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Image Enhancement
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Image Enhancement is basically improving the interpretability or perception of information in images for human viewers and providing ‘better’ input for other automated image processing techniques. The principal objective of Image Enhancement is to modify attributes of an image to make it more suitable for a given task and a specific observer.
Source: A Comprehensive Review of Image Enhancement Techniques
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
MAC-Lookup: Multi-Axis Conditional Lookup Model for Underwater Image Enhancement
Learning to See in the Extremely Dark
TDiR: Transformer based Diffusion for Image Restoration Tasks
A Multi-Scale Spatial Attention-Based Zero-Shot Learning Framework for Low-Light Image Enhancement
Temperature calibration of surface emissivities with an improved thermal image enhancement network
DreamLight: Towards Harmonious and Consistent Image Relighting
DREAM: On hallucinations in AI-generated content for nuclear medicine imaging
Physics Informed Capsule Enhanced Variational AutoEncoder for Underwater Image Enhancement
A Poisson-Guided Decomposition Network for Extreme Low-Light Image Enhancement
RT-X Net: RGB-Thermal cross attention network for Low-Light Image Enhancement
URWKV: Unified RWKV Model with Multi-state Perspective for Low-light Image Restoration
CURVE: CLIP-Utilized Reinforcement Learning for Visual Image Enhancement via Simple Image Processing
See through the Dark: Learning Illumination-affined Representations for Nighttime Occupancy Prediction
Deep learning based spatial aliasing reduction in beamforming for audio capture
Underwater Diffusion Attention Network with Contrastive Language-Image Joint Learning for Underwater Image Enhancement
Improving Novel view synthesis of 360$^\circ$ Scenes in Extremely Sparse Views by Jointly Training Hemisphere Sampled Synthetic Images
Forward-only Diffusion Probabilistic Models
Degradation-Aware Feature Perturbation for All-in-One Image Restoration
Entropy-Driven Genetic Optimization for Deep-Feature-Guided Low-Light Image Enhancement
PDE: Gene Effect Inspired Parameter Dynamic Evolution for Low-light Image Enhancement
Super-Resolution Generative Adversarial Networks based Video Enhancement
A review of advancements in low-light image enhancement using deep learning
ADNP-15: An Open-Source Histopathological Dataset for Neuritic Plaque Segmentation in Human Brain Whole Slide Images with Frequency Domain Image Enhancement for Stain Normalization
TS-Diff: Two-Stage Diffusion Model for Low-Light RAW Image Enhancement
MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection
HiLLIE: Human-in-the-Loop Training for Low-Light Image Enhancement
Multi-Scale Target-Aware Representation Learning for Fundus Image Enhancement
RD-UIE: Relation-Driven State Space Modeling for Underwater Image Enhancement
VR-FuseNet: A Fusion of Heterogeneous Fundus Data and Explainable Deep Network for Diabetic Retinopathy Classification
DeepSPG: Exploring Deep Semantic Prior Guidance for Low-light Image Enhancement with Multimodal Learning
Adaptive Dual-domain Learning for Underwater Image Enhancement
FusionNet: Multi-model Linear Fusion Framework for Low-light Image Enhancement
SonarT165: A Large-scale Benchmark and STFTrack Framework for Acoustic Object Tracking
Structure-guided Diffusion Transformer for Low-Light Image Enhancement
Towards Scale-Aware Low-Light Enhancement via Structure-Guided Transformer Design
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