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/manishsoni1908/mobilenet-ssd-keras
https://github.com/manjitullal/DE-GAN
DE-GAN: A Conditional Generative Adversarial Network for Document Enhancement
https://github.com/mducducd/Shadow-Removal
https://github.com/mducducd/ghost-free-shadow-removal
https://github.com/mediatek-neuropilot/mai22-real-time-video-sr
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
https://github.com/megvii-research/caddm
https://github.com/millanp95/OFTracker
https://github.com/mindspore-ai/contrib/tree/master/intern/Luminance-Guided-Chrominance-Enhancement-for-HEVC-Intra-Coding
Luminance-Guided Chrominance Image Enhancement for HEVC Intra Coding
https://github.com/mindspore-ai/contrib/tree/master/papers/UColor
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/mindspore-ai/contrib/tree/master/papers/Zero-DCE
Low-Light Image and Video Enhancement Using Deep Learning: A Survey
https://github.com/mindspore-ai/contrib/tree/master/papers/Zero-DCE%2B%2B
Learning to Enhance Low-Light Image via Zero-Reference Deep Curve Estimation
https://github.com/mindspore-ai/models/tree/master/official/cv/ssd
https://github.com/mindspore-ai/models/tree/master/research/cv/ssd_resnet50
https://github.com/minhhuu291/Logo-Recognition
https://github.com/miteshksingh/SimplifiedYOLO
https://github.com/ml-inory/Caffe
https://github.com/mmfrxx/X-net
https://github.com/mrmtn86/python1
https://github.com/naoya0731/deepmosaic
https://github.com/naoya0731/deepmozaik_sample
https://github.com/nasirtrekker/personcounter
https://github.com/natuan310/scale-recurrent-network-images-deblurring
https://github.com/navganti/SIVO
https://github.com/navganti/SegNet
https://github.com/nikhilsid/CV_HW5
https://github.com/nirajdevpandey/Object-detection-and-localization-using-SSD-
https://github.com/nirajdpandey/Object-detection-and-localization-using-SSD
https://github.com/nope0124/ssd_keras_hisakawa
https://github.com/nshaud/DeepNetsForEO
https://github.com/nsom/ssd
https://github.com/nutintin/Robotic_SP2019
https://github.com/nvlabs/tiny-cuda-nn
Extracting Triangular 3D Models, Materials, and Lighting From Images
https://github.com/object-detection-algorithm/SSD
https://github.com/onpix/LCDPNet
https://github.com/open-mmlab/mmdetection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.