https://github.com/mowen111/salt
공식 코드Small or Far Away? Exploiting Deep Super-Resolution and Altitude Data for Aerial Animal Surveillance
Papers with Code 아카이브
3D Object Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 0
- 보존된 평가 기록
- 0
3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/mr-bamboo/mfg-hmoe
공식 코드Heterogeneous Mixture of Experts for Remote Sensing Image Super-Resolution
https://github.com/msu-video-group/erqa
공식 코드ERQA: Edge-Restoration Quality Assessment for Video Super-Resolution
https://github.com/msu-video-group/srdm
공식 코드Combining Contrastive and Supervised Learning for Video Super-Resolution Detection
https://github.com/multimedialabsfu/learned-point-cloud-compression-for-classification
공식 코드https://github.com/muneebaadil/sisr-irl
공식 코드Improving Super-Resolution Methods via Incremental Residual Learning
https://github.com/mv-lab/AISP
공식 코드Reversed Image Signal Processing and RAW Reconstruction. AIM 2022 Challenge Report
https://github.com/mv-lab/swin2sr
공식 코드AIM 2022 Challenge on Super-Resolution of Compressed Image and Video: Dataset, Methods and Results
https://github.com/naagar/art_restoration_dm
공식 코드Adaptation of the super resolution SOTA for Art Restoration in camera capture images
https://github.com/nalemadi/VME_CDSI_dataset_benchmark
공식 코드VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
https://github.com/nana01219/geodsr
공식 코드Learning Continuous Depth Representation via Geometric Spatial Aggregator
https://github.com/nanmehta/gmtnet
공식 코드Gated Multi-Resolution Transfer Network for Burst Restoration and Enhancement
https://github.com/natezhenghy/udke
공식 코드Unfolded Deep Kernel Estimation for Blind Image Super-resolution
https://github.com/nathan66666/dvmsr
공식 코드DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
https://github.com/nathan66666/hasn
공식 코드HASN: Hybrid Attention Separable Network for Efficient Image Super-resolution
https://github.com/nathzi1505/forecast-diffmodels
공식 코드Forecasting Tropical Cyclones with Cascaded Diffusion Models
https://github.com/ncarraz/AFILM
공식 코드https://github.com/ncarraz/ESRGANplus
공식 코드ESRGAN+ : Further Improving Enhanced Super-Resolution Generative Adversarial Network
https://github.com/ncherel/psal
공식 코드https://github.com/necludov/jam
공식 코드https://github.com/neoncloud/mdctgan
공식 코드mdctGAN: Taming transformer-based GAN for speech super-resolution with Modified DCT spectra
https://github.com/neural-video-delivery/emt-pytorch-eccv2022
공식 코드Efficient Meta-Tuning for Content-aware Neural Video Delivery
https://github.com/ngchc/CameraSR
공식 코드https://github.com/nichenxingmeng/STDAN
공식 코드Spatio-Temporal Distortion Aware Omnidirectional Video Super-Resolution
https://github.com/nihalsid/retrieval-fuse
공식 코드RetrievalFuse: Neural 3D Scene Reconstruction with a Database
https://github.com/nikhilvravi/DukeSR
공식 코드https://github.com/nilotpal09/superresolutionhep
공식 코드Denoising Graph Super-Resolution towards Improved Collider Event Reconstruction
https://github.com/niopeng/CHIMLE
공식 코드CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis
https://github.com/nithin-gk/at-ddpm
공식 코드https://github.com/nju-jet/fmen
공식 코드Fast and Memory-Efficient Network Towards Efficient Image Super-Resolution
https://github.com/njulj/AdaDM
공식 코드https://github.com/njulj/RFDN
공식 코드Residual Feature Distillation Network for Lightweight Image Super-Resolution
https://github.com/nmhkahn/PCARN-pytorch
공식 코드Efficient Deep Neural Network for Photo-realistic Image Super-Resolution
https://github.com/nonozhizhiovo/realarbisr
공식 코드https://github.com/nonwhy/pure
공식 코드https://github.com/norskregnesentral/difffusr
공식 코드DiffFuSR: Super-Resolution of all Sentinel-2 Multispectral Bands using Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.