https://github.com/NVlabs/nvdiffrec
공식 코드Extracting Triangular 3D Models, Materials, and Lighting From Images
Papers with Code 아카이브
Image Enhancement
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 10
- 평가표
- 7
- 보존된 평가 기록
- 26
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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/NVlabs/nvdiffrecmc
공식 코드Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising
https://github.com/Nickyang4900/Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding
공식 코드Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding
https://github.com/OUCVisionGroup/CLIP-UIE
공식 코드Underwater Image Enhancement by Diffusion Model with Customized CLIP-Classifier
https://github.com/OcraM17/eXIE
공식 코드Explaining Image Enhancement Black-Box Methods through a Path Planning Based Algorithm
https://github.com/PREDICT-EPFL/polydome
공식 코드Data-driven input reconstruction and experimental validation
https://github.com/Pangyk/SinoSynth
공식 코드SinoSynth: A Physics-based Domain Randomization Approach for Generalizable CBCT Image Enhancement
https://github.com/Paul92/cp_2020
공식 코드https://github.com/Pengyu-gis/Historical-Aerial-Photos
공식 코드A GAN-Enhanced Deep Learning Framework for Rooftop Detection from Historical Aerial Imagery
https://github.com/QihanZhao/enlighten-anything
공식 코드Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
https://github.com/QingXin96/Intrinsic_image_transfer
공식 코드https://github.com/RQ-Wu/UnderwaterRanker
공식 코드Underwater Ranker: Learn Which Is Better and How to Be Better
https://github.com/RisabBiswas/T2T-BinFormer
공식 코드A Layer-Wise Tokens-to-Token Transformer Network for Improved Historical Document Image Enhancement
https://github.com/Royalvice/DocDiff
공식 코드https://github.com/Royalvice/GDB
공식 코드https://github.com/RyanXingQL/RBQE
공식 코드Early Exit or Not: Resource-Efficient Blind Quality Enhancement for Compressed Images
https://github.com/SahilAliAkbar/Image_Noise_Analysis
공식 코드Analyzing Noise Models and Advanced Filtering Algorithms for Image Enhancement
https://github.com/Sekeh-Lab/FogGuard
공식 코드https://github.com/SheepHow/ELIE_STR
공식 코드Extremely Low-light Image Enhancement with Scene Text Restoration
https://github.com/ShenZheng2000/Semantic-Guided-Low-Light-Image-Enhancement
공식 코드Semantic-Guided Zero-Shot Learning for Low-Light Image/Video Enhancement
https://github.com/ShreeramAthreya/CDX-USChallenge
공식 코드Ultrasound Image Enhancement using CycleGAN and Perceptual Loss
https://github.com/Shyandram/CPGA-Net_Plus
공식 코드https://github.com/SinaRaoufi/CDAN
공식 코드CDAN: Convolutional dense attention-guided network for low-light image enhancement
https://github.com/StanfordORB/Stanford-ORB
공식 코드Stanford-ORB: A Real-World 3D Object Inverse Rendering Benchmark
https://github.com/TalRemez/deep_class_aware_denoising
공식 코드https://github.com/TingdiRen/URSCT-SESR
공식 코드Reinforced Swin-Convs Transformer for Underwater Image Enhancement
https://github.com/TsaiZinan/CoDER_Project_Code
공식 코드https://github.com/VinAIResearch/CPM
공식 코드Lipstick ain't enough: Beyond Color Matching for In-the-Wild Makeup Transfer
https://github.com/Vishal-V/FSDA-LowLight
공식 코드Few-Shot Domain Adaptation for Low Light RAW Image Enhancement
https://github.com/WISION-Lab/event-nn-pytorch
공식 코드https://github.com/WISION-Lab/event-nn-tf
공식 코드https://github.com/WangLiwen1994/DeepRelight
공식 코드Deep Relighting Networks for Image Light Source Manipulation
https://github.com/YhuoyuH/RetinexMamba
공식 코드Retinexmamba: Retinex-based Mamba for Low-light Image Enhancement
https://github.com/ZHKKKe/NeuralPreset
공식 코드https://github.com/ZHKKKe/PixelSSL
공식 코드Guided Collaborative Training for Pixel-wise Semi-Supervised Learning
https://github.com/ZPDu/Boosting-Object-Detection-with-Zero-Shot-Day-Night-Domain-Adaptation
공식 코드Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.