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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xpixelgroup/diffbir
공식 코드DiffBIR: Towards Blind Image Restoration with Generative Diffusion Prior
https://github.com/xpixelgroup/hat
공식 코드Activating More Pixels in Image Super-Resolution Transformer
https://github.com/xuefusiji/badrefsr
공식 코드BadRefSR: Backdoor Attacks Against Reference-based Image Super Resolution
https://github.com/xuxy09/RawSR
공식 코드https://github.com/xy-boy/ediffsr
공식 코드EDiffSR: An Efficient Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution
https://github.com/xylgroup/mlmc
공식 코드Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
https://github.com/xzwlx/Difficulty-SR
공식 코드Difficulty-aware Image Super Resolution via Deep Adaptive Dual-Network
https://github.com/yandex-research/distill-nf
공식 코드https://github.com/yandex-research/gan_vs_diff_sr
공식 코드https://github.com/yangbincheng/emsrdpn
공식 코드Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning
https://github.com/yangxy/pasd
공식 코드Pixel-Aware Stable Diffusion for Realistic Image Super-resolution and Personalized Stylization
https://github.com/yangyanggirl/bos
공식 코드Bounding Box Stability against Feature Dropout Reflects Detector Generalization across Environments
https://github.com/yaozzz666/dscf-sr
공식 코드Distillation-Supervised Convolutional Low-Rank Adaptation for Efficient Image Super-Resolution
https://github.com/yaziwel/region-attention-transformer-for-medical-image-restoration
공식 코드https://github.com/yaziwel/restore-rwkv
공식 코드Restore-RWKV: Efficient and Effective Medical Image Restoration with RWKV
https://github.com/ychfan/nsr
공식 코드https://github.com/ychfan/scn
공식 코드https://github.com/yi-yang355/frfsr
공식 코드A Feature Reuse Framework with Texture-adaptive Aggregation for Reference-based Super-Resolution
https://github.com/yinboc/liif
공식 코드Learning Continuous Image Representation with Local Implicit Image Function
https://github.com/yingqianwang/lf-danet
공식 코드Real-World Light Field Image Super-Resolution via Degradation Modulation
https://github.com/yingqianwang/lf-dmnet
공식 코드Real-World Light Field Image Super-Resolution via Degradation Modulation
https://github.com/yingutk/u2MDN
공식 코드Unsupervised and Unregistered Hyperspectral Image Super-Resolution with Mutual Dirichlet-Net
https://github.com/yinzhicun/metaf2n
공식 코드MetaF2N: Blind Image Super-Resolution by Learning Efficient Model Adaptation from Faces
https://github.com/yjsunnn/fbanet
공식 코드Towards Real-World Burst Image Super-Resolution: Benchmark and Method
https://github.com/ykshi/DDet
공식 코드DDet: Dual-path Dynamic Enhancement Network for Real-World Image Super-Resolution
https://github.com/yongliuy/ditn
공식 코드Unfolding Once is Enough: A Deployment-Friendly Transformer Unit for Super-Resolution
https://github.com/yongliuy/patchscaler
공식 코드PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution
https://github.com/yongsongh/aidsrgan-miccai2022
공식 코드Rethinking Degradation: Radiograph Super-Resolution via AID-SRGAN
https://github.com/yongsongh/dasrgan
공식 코드Texture and Noise Dual Adaptation for Infrared Image Super-Resolution
https://github.com/yongsongh/infrared_image_sr_survey
공식 코드Infrared Image Super-Resolution: Systematic Review, and Future Trends
https://github.com/yongsongh/irsrmamba
공식 코드https://github.com/yuan-yutao/ecdp
공식 코드Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
https://github.com/yuanyunshuang/fpv_rcnn
공식 코드Keypoints-Based Deep Feature Fusion for Cooperative Vehicle Detection of Autonomous Driving
https://github.com/yuanzhi-zhu/oftsr
공식 코드OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
https://github.com/yuehan717/pdasr
공식 코드Perception-Distortion Balanced ADMM Optimization for Single-Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.