https://github.com/raspstephan/nwp-downscale
공식 코드Increasing the accuracy and resolution of precipitation forecasts using deep generative models
Papers with Code 아카이브
3D Object Super-Resolution
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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/rayabhisek123/cfat
공식 코드CFAT: Unleashing TriangularWindows for Image Super-resolution
https://github.com/razvanmarinescu/brgm
공식 코드https://github.com/razvanmarinescu/brgm-pytorch
공식 코드https://github.com/redrock303/revisiting-temporal-alignment-for-video-restoration
공식 코드https://github.com/ree1s/idm
공식 코드https://github.com/renyang-home/ldv_dataset
공식 코드https://github.com/renyang-home/ntire22_venh_sr
공식 코드https://github.com/renyulin-f/mambacsr
공식 코드MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
https://github.com/researchmm/TTSR
공식 코드Learning Texture Transformer Network for Image Super-Resolution
https://github.com/researchmm/TTVSR
공식 코드Learning Trajectory-Aware Transformer for Video Super-Resolution
https://github.com/researchmm/ftvsr
공식 코드Learning Spatiotemporal Frequency-Transformer for Compressed Video Super-Resolution
https://github.com/researchmm/vqd-sr
공식 코드Learning Data-Driven Vector-Quantized Degradation Model for Animation Video Super-Resolution
https://github.com/rexzhan/essaformer
공식 코드ESSAformer: Efficient Transformer for Hyperspectral Image Super-resolution
https://github.com/rezanajian/fol
공식 코드https://github.com/rfonod/geo-trax
공식 코드Advanced computer vision for extracting georeferenced vehicle trajectories from drone imagery
https://github.com/riccardodelutio/PixTransform
공식 코드https://github.com/riccardomarin/InstantRecoveryFromSpectrum
공식 코드Instant recovery of shape from spectrum via latent space connections
https://github.com/rishavbb/owslr
공식 코드OW-SLR: Overlapping Windows on Semi-Local Region for Image Super-Resolution
https://github.com/risingentropy/lpfinisr
공식 코드Exploring the Low-Pass Filtering Behavior in Image Super-Resolution
https://github.com/robbiewatt1/climatediffuse
공식 코드https://github.com/robotic-vision-lab/Attention-With-Varying-Receptive-Fields-Network
공식 코드Thermal Image Super-Resolution Using Second-Order Channel Attention with Varying Receptive Fields
https://github.com/robotic-vision-lab/dual-interactive-implicit-neural-network
공식 코드Single Image Super-Resolution via a Dual Interactive Implicit Neural Network
https://github.com/roldbach/autoencoder-3d-ct-sisr
공식 코드https://github.com/rolnicklab/constrained-downscaling
공식 코드https://github.com/romanjacome99/ld_gan
공식 코드https://github.com/roninton/anr
공식 코드Attention Beats Linear for Fast Implicit Neural Representation Generation
https://github.com/royson/metakernelgan
공식 코드Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation
https://github.com/rozumden/DeFMO
공식 코드https://github.com/rozumden/ShapeFromBlur
공식 코드Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects
https://github.com/rozumden/deblatting_python
공식 코드https://github.com/rozumden/fmo-cpp-demo
공식 코드https://github.com/rozumden/fmo-deblurring-benchmark
공식 코드https://github.com/rs-lsl/cssnet
공식 코드https://github.com/rubbbbbbbbby/swinoir
공식 코드https://github.com/ruizhecao96/cmgan
공식 코드CMGAN: Conformer-Based Metric-GAN for Monaural Speech Enhancement
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.