https://github.com/ucaszyp/steps
공식 코드STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
STEPS: Joint Self-supervised Nighttime Image Enhancement and Depth Estimation
UIERL: Internal-External Representation Learning Network for Underwater Image Enhancement
Efficient Neural Network Encoding for 3D Color Lookup Tables
RSFNet: A White-Box Image Retouching Approach using Region-Specific Color Filters
Underwater Image Enhancement by Convolutional Spiking Neural Networks
PSENet: Progressive Self-Enhancement Network for Unsupervised Extreme-Light Image Enhancement
Bilevel Fast Scene Adaptation for Low-Light Image Enhancement
Toward Fast, Flexible, and Robust Low-Light Image Enhancement
HighlightNet: Highlighting Low-Light Potential Features for Real-Time UAV Tracking
Regularizing Nighttime Weirdness: Efficient Self-supervised Monocular Depth Estimation in the Dark
FourLLIE: Boosting Low-Light Image Enhancement by Fourier Frequency Information
DPEC: Dual-Path Error Compensation Method for Enhanced Low-Light Image Clarity
Semantic-aware Texture-Structure Feature Collaboration for Underwater Image Enhancement
Multi-modal Bifurcated Network for Depth Guided Image Relighting
YAYI-UIE: A Chat-Enhanced Instruction Tuning Framework for Universal Information Extraction
Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
Underwater Variable Zoom: Depth-Guided Perception Network for Underwater Image Enhancement
Dimma: Semi-supervised Low Light Image Enhancement with Adaptive Dimming
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
MambaNUT: Nighttime UAV Tracking via Mamba-based Adaptive Curriculum Learning
ExposureDiffusion: Learning to Expose for Low-light Image Enhancement
Simultaneous Enhancement and Super-Resolution of Underwater Imagery for Improved Visual Perception
Fast Underwater Image Enhancement for Improved Visual Perception
Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation
Low-Light Image Enhancement via Structure Modeling and Guidance
Privacy-Preserving Pose Estimation for Human-Robot Interaction
Image Enhancement for Remote Photoplethysmography in a Low-Light Environment
A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis
ALEN: A Dual-Approach for Uniform and Non-Uniform Low-Light Image Enhancement
Relighting Images in the Wild with a Self-Supervised Siamese Auto-Encoder
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.