https://github.com/nqian1/survey-on-odisr-and-odvsr
공식 코드A Systematic Investigation on Deep Learning-Based Omnidirectional Image and Video Super-Resolution
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Super-Resolution is a task in computer vision that involves increasing the resolution of an image or video by generating missing high-frequency details from low-resolution input. The goal is to produce an output image with a higher resolution than the input image, while preserving the original content and structure.
<span style="color:grey; opacity: 0.6">( Credit: MemNet )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/nthuy190991/SRSM_QuebecCity_building_extraction
공식 코드https://github.com/nvlabs/i2sb
공식 코드https://github.com/observethesun/PIL
공식 코드Machine learning for reconstruction of polarity inversion lines from solar filaments
https://github.com/ofsoundof/3D_Appearance_SR
공식 코드https://github.com/ofsoundof/dhp
공식 코드https://github.com/ofsoundof/grl-image-restoration
공식 코드Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
https://github.com/ofsoundof/imdn
공식 코드NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/ofsoundof/learning_filter_basis
공식 코드Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ofsoundof/ntire2022_esr
공식 코드NTIRE 2022 Challenge on Efficient Super-Resolution: Methods and Results
https://github.com/ohayonguy/PMRF
공식 코드Posterior-Mean Rectified Flow: Towards Minimum MSE Photo-Realistic Image Restoration
https://github.com/oldsweet/hsrmamba
공식 코드HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution
https://github.com/omaralezaby/mimicked_ali
공식 코드Enhanced Super-Resolution Training via Mimicked Alignment for Real-World Scenes
https://github.com/omerb01/puq
공식 코드Principal Uncertainty Quantification with Spatial Correlation for Image Restoration Problems
https://github.com/omnidirectional-video-group/odvista
공식 코드ODVista: An Omnidirectional Video Dataset for super-resolution and Quality Enhancement Tasks
https://github.com/open-mmlab/mmediting
공식 코드BasicVSR++: Improving Video Super-Resolution with Enhanced Propagation and Alignment
https://github.com/openai/consistency_models
공식 코드https://github.com/ozgurkara99/ISNAS-DIP
공식 코드ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
https://github.com/palmdong/mmsr
공식 코드Learning Mutual Modulation for Self-Supervised Cross-Modal Super-Resolution
https://github.com/pancakewaffles/metasrgan-tutorial
공식 코드https://github.com/parkseobin/MLSR
공식 코드Fast Adaptation to Super-Resolution Networks via Meta-Learning
https://github.com/passerer/hpinet
공식 코드From Coarse to Fine: Hierarchical Pixel Integration for Lightweight Image Super-Resolution
https://github.com/patrisvalera/slnet_storm
공식 코드Sparsity-based background removal for STORM super-resolution images
https://github.com/paulalbert31/clover_ssl
공식 코드https://github.com/paulpuren/physr
공식 코드Physics-informed Deep Super-resolution for Spatiotemporal Data
https://github.com/pbehjatii/OverNet
공식 코드OverNet: Lightweight Multi-Scale Super-Resolution with Overscaling Network
https://github.com/peirong26/Brain-ID
공식 코드Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
https://github.com/pengsongyou/SRmeetsPS
공식 코드Depth Super-Resolution Meets Uncalibrated Photometric Stereo
https://github.com/peylnog/ERealSR-DGPNet
공식 코드Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution
https://github.com/pfnet-research/multi-stage-blended-diffusion
공식 코드High-Resolution Image Editing via Multi-Stage Blended Diffusion
https://github.com/pfnet-research/sngan_projection
공식 코드https://github.com/piaacaa/temporal4dflownet
공식 코드https://github.com/pic4ser/edgesrgan
공식 코드Generative Adversarial Super-Resolution at the Edge with Knowledge Distillation
https://github.com/picsart-ai-research/hd-painter
공식 코드HD-Painter: High-Resolution and Prompt-Faithful Text-Guided Image Inpainting with Diffusion Models
https://github.com/picsart-ai-research/videoinr-continuous-space-time-super-resolution
공식 코드VideoINR: Learning Video Implicit Neural Representation for Continuous Space-Time Super-Resolution
https://github.com/pigyang-ops/cyclegan-qsdl
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.