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Exposure Correction
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Learning to Harmonize Cross-vendor X-ray Images by Non-linear Image Dynamics Correction
Discovering an Image-Adaptive Coordinate System for Photography Processing
HipyrNet: Hypernet-Guided Feature Pyramid network for mixed-exposure correction
Learning Adaptive Lighting via Channel-Aware Guidance
Luminance Component Analysis for Exposure Correction
OSMamba: Omnidirectional Spectral Mamba with Dual-Domain Prior Generator for Exposure Correction
ECMamba: Consolidating Selective State Space Model with Retinex Guidance for Efficient Multiple Exposure Correction
Retinex-RAWMamba: Bridging Demosaicing and Denoising for Low-Light RAW Image Enhancement
EndoUIC: Promptable Diffusion Transformer for Unified Illumination Correction in Capsule Endoscopy
Color Shift Estimation-and-Correction for Image Enhancement
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance
Deep unfolding Network for Hyperspectral Image Super-Resolution with Automatic Exposure Correction
Region-Aware Exposure Consistency Network for Mixed Exposure Correction
Learning Exposure Correction in Dynamic Scenes
FD-Vision Mamba for Endoscopic Exposure Correction
4K-Resolution Photo Exposure Correction at 125 FPS with ~8K Parameters
CNN Injected Transformer for Image Exposure Correction
Holistic Dynamic Frequency Transformer for Image Fusion and Exposure Correction
Fearless Luminance Adaptation: A Macro-Micro-Hierarchical Transformer for Exposure Correction
Deep learning-based image exposure enhancement as a pre-processing for an accurate 3D colon surface reconstruction
Practical Exposure Correction: Great Truths Are Always Simple
ExReg: Wide-range Photo Exposure Correction via a Multi-dimensional Regressor with Attention
Multi-Scale Structural-aware Exposure Correction for Endoscopic Imaging
R2C-GAN: Restore-to-Classify Generative Adversarial Networks for Blind X-Ray Restoration and COVID-19 Classification
DRL-ISP: Multi-Objective Camera ISP with Deep Reinforcement Learning
You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction
Exposure Correction Model to Enhance Image Quality
FCNet: A Convolutional Neural Network for Arbitrary-Length Exposure Estimation
Learning to Adapt to Light
A Survey on Deep learning based Document Image Enhancement
Frequency Domain Loss Function for Deep Exposure Correction of Dark Images
A Two-stage Deep Network for High Dynamic Range Image Reconstruction
Learning Multi-Scale Photo Exposure Correction
Dual Illumination Estimation for Robust Exposure Correction
Deep Camera: A Fully Convolutional Neural Network for Image Signal Processing
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