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/2023-MindSpore-1/ms-code-18/tree/main/llnet
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
https://github.com/2023-MindSpore-1/ms-code-32
https://github.com/2023-MindSpore-4/Code-5/tree/main/llnet
LLNet: A Deep Autoencoder Approach to Natural Low-light Image Enhancement
https://github.com/2023-MindSpore-4/Code10/tree/main/SSD
https://github.com/2023-MindSpore-4/Code4/tree/main/JCRNet-mindspore-main
Joint Correcting and Refinement for Balanced Low-Light Image Enhancement
https://github.com/59Kkk/pytorch_Ucolor_lcy
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/AVAuco/ssd_people
https://github.com/AbhinavJhanwar/object-detection-classification
https://github.com/AlbertZhangHIT/CNN-POCS
Can learning from natural image denoising be used for seismic data interpolation?
https://github.com/AmirDavoodi/Hand-Gestures-Human-Robot-Interaction
https://github.com/Ander02/FaceRecognition
https://github.com/Andrew-Qibin/ssdlite-pytorch
https://github.com/Boyiliee/PONO
Underwater Image Enhancement via Learning Water Type Desensitized Representations
https://github.com/BushrHaddad/Color-Image-Enhancement-Using-the-Support-Fuzzification
Color Image Enhancement Using the lrgb Coordinates in the Context of Support Fuzzification
https://github.com/CLEANit/SRS2021
https://github.com/CV-Reimplementation/Ucolor-Reimplementation
Underwater Image Enhancement via Medium Transmission-Guided Multi-Color Space Embedding
https://github.com/CV-deeplearning/caffe
https://github.com/Chubbyman2/SSD_MobileNet_Hand_Tracker
https://github.com/ChujoHiroto/tensorflow-ssd
https://github.com/ChunML/ssd-tf2
https://github.com/Coldmooon/SSD-on-Custom-Dataset
https://github.com/Cosmos218/ssd-caffe
https://github.com/CycloneBoy/PPDetectionPytorch
https://github.com/DChaushev/Age-Gender-Recognition
https://github.com/Deci-AI/super-gradients
https://github.com/DeniJsonC/PPformer
https://github.com/DeniJsonC/WaveNet
https://github.com/DmitryRyumin/ICCV-2023-Papers
Retinexformer: One-stage Retinex-based Transformer for Low-light Image Enhancement
https://github.com/Eljefemasao/Customed-Object-Detector
https://github.com/Eljefemasao/MarkNet-ObjectDetector
https://github.com/EricYang3721/faces
https://github.com/Fangrn/caffe-segnet
https://github.com/GatorSense/SRrootimaging
https://github.com/GinZhu/MIASSR
https://github.com/GinZhu/RDST
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.