https://github.com/yangxy/pasd
공식 코드Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
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/yanzq95/liar
공식 코드https://github.com/yazdaniamir38/Depth-guided-Image-Relighting
공식 코드https://github.com/yebulabula/disc-diff
공식 코드DisC-Diff: Disentangled Conditional Diffusion Model for Multi-Contrast MRI Super-Resolution
https://github.com/ygjwd12345/BiMix
공식 코드Bi-Mix: Bidirectional Mixing for Domain Adaptive Nighttime Semantic Segmentation
https://github.com/yh-yoon/gmt
공식 코드GMT: Enhancing Generalizable Neural Rendering via Geometry-Driven Multi-Reference Texture Transfer
https://github.com/yiyulics/csec
공식 코드https://github.com/yongsongh/irsrmamba
공식 코드https://github.com/ysz2022/nerco
공식 코드Implicit Neural Representation for Cooperative Low-light Image Enhancement
https://github.com/yu-li/AGLLNet
공식 코드Attention Guided Low-light Image Enhancement with a Large Scale Low-light Simulation Dataset
https://github.com/yueatsprograms/ttt_cifar_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/yueatsprograms/ttt_imagenet_release
공식 코드Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/yueruchen/EnlightenGAN
공식 코드EnlightenGAN: Deep Light Enhancement without Paired Supervision
https://github.com/yupeilin2388/diff-mosaic
공식 코드https://github.com/yuxin-zhang-jasmine/ius2024_diffusion
공식 코드Ultrasound Image Enhancement with the Variance of Diffusion Models
https://github.com/yzbouc/uveb
공식 코드UVEB: A Large-scale Benchmark and Baseline Towards Real-World Underwater Video Enhancement
https://github.com/yzxing87/pytorch-deep-video-prior
공식 코드https://github.com/zengxizhang/hupe
공식 코드HUPE: Heuristic Underwater Perceptual Enhancement with Semantic Collaborative Learning
https://github.com/zhangbaijin/enlighten-anything
공식 코드Enlighten Anything: When Segment Anything Model Meets Low-Light Image Enhancement
https://github.com/zhangsong1213/pdcfnet
공식 코드PDCFNet: Enhancing Underwater Images through Pixel Difference Convolution
https://github.com/zhaoyuzhi/Legacy-Photo-Editing-with-Learned-Noise-Prior
공식 코드https://github.com/zheng980629/cue
공식 코드Empowering Low-Light Image Enhancer through Customized Learnable Priors
https://github.com/zhengdharia/LUD-VAE
공식 코드Learn from Unpaired Data for Image Restoration: A Variational Bayes Approach
https://github.com/zhenqifu/Twice-Mixing
공식 코드Twice Mixing: A Rank Learning based Quality Assessment Approach for Underwater Image Enhancement
https://github.com/zhenqifu/puie-net
공식 코드https://github.com/zhihefang/SFGNet
공식 코드Toward Sufficient Spatial-Frequency Interaction for Gradient-aware Underwater Image Enhancement
https://github.com/zhihefang/wf-diff
공식 코드https://github.com/zhling2020/RIS-GAN
공식 코드RIS-GAN: Explore Residual and Illumination with Generative Adversarial Networks for Shadow Removal
https://github.com/zhoujingchun03/smdr-is
공식 코드Synergistic Multiscale Detail Refinement via Intrinsic Supervision for Underwater Image Enhancement
https://github.com/zhuangyunliang/dpfnet
공식 코드https://github.com/zjunlp/adakgc
공식 코드https://github.com/zpdu/dai-net
공식 코드Boosting Object Detection with Zero-Shot Day-Night Domain Adaptation
https://github.com/ztMotaLee/Sagiri
공식 코드Sagiri: Low Dynamic Range Image Enhancement with Generative Diffusion Prior
https://github.com/zzr-idam/mixnet
공식 코드MixNet: Efficient Global Modeling for Ultra-High-Definition Image Restoration
https://gitee.com/HarveyYeung/PUGAN_MindSpore.git
PUGAN: Physical Model-Guided Underwater Image Enhancement Using GAN with Dual-Discriminators
https://github.com/111qqz/caffe-ssd
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.