Papers with Code 아카이브
Super-Resolution
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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/caojiezhang/ciaosr
공식 코드https://github.com/caojiezhang/datsr
공식 코드Reference-based Image Super-Resolution with Deformable Attention Transformer
https://github.com/caojiezhang/davsr
공식 코드Towards Interpretable Video Super-Resolution via Alternating Optimization
https://github.com/caoscott/SReC
공식 코드https://github.com/carlos-gg/dl4ds
공식 코드https://github.com/casus/cvdm
공식 코드https://github.com/cd-athena/movidnn
공식 코드MoViDNN: A Mobile Platform for Evaluating Video Quality Enhancement with Deep Neural Networks
https://github.com/ceciliavision/zoom-learn-zoom
공식 코드https://github.com/centreborelli/L1BSR
공식 코드https://github.com/chaofengc/Face-SPARNet
공식 코드https://github.com/chaofengc/femasr
공식 코드Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
https://github.com/chaofengc/iter
공식 코드Iterative Token Evaluation and Refinement for Real-World Super-Resolution
https://github.com/chaofengc/quantexsr
공식 코드Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors
https://github.com/chaoma99/sr-metric
공식 코드Learning a No-Reference Quality Metric for Single-Image Super-Resolution
https://github.com/chaowentao/lfsrdiff
공식 코드LFSRDiff: Light Field Image Super-Resolution via Diffusion Models
https://github.com/chaowentao/maskblur
공식 코드MaskBlur: Spatial and Angular Data Augmentation for Light Field Image Super-Resolution
https://github.com/charlesnchr/ML-SIM
공식 코드ML-SIM: A deep neural network for reconstruction of structured illumination microscopy images
https://github.com/charlesnchr/vsr-sim
공식 코드Spatio-temporal Vision Transformer for Super-resolution Microscopy
https://github.com/cheeun/cadyq
공식 코드CADyQ: Content-Aware Dynamic Quantization for Image Super-Resolution
https://github.com/cheeun/odm
공식 코드Overcoming Distribution Mismatch in Quantizing Image Super-Resolution Networks
https://github.com/chenbong/arm-net
공식 코드https://github.com/chenpengseu/sdoa-net
공식 코드SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array
https://github.com/chenpengseu/sdoanet
공식 코드SDOA-Net: An Efficient Deep Learning-Based DOA Estimation Network for Imperfect Array
https://github.com/chensong1995/deblursr
공식 코드DeblurSR: Event-Based Motion Deblurring Under the Spiking Representation
https://github.com/chentsaimin/SRECG
공식 코드https://github.com/chiyich/egovsr
공식 코드EgoVSR: Towards High-Quality Egocentric Video Super-Resolution
https://github.com/chomeyama/DualCycleGAN
공식 코드Nonparallel High-Quality Audio Super Resolution with Domain Adaptation and Resampling CycleGANs
https://github.com/chrisdud0257/gsasr
공식 코드Generalized and Efficient 2D Gaussian Splatting for Arbitrary-scale Super-Resolution
https://github.com/chrisdud0257/hggt
공식 코드Human Guided Ground-truth Generation for Realistic Image Super-resolution
https://github.com/chrisdud0257/ssl
공식 코드SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
https://github.com/christianschuessler/sparse-array-radar-imaging
공식 코드https://github.com/chuhuaxian/HF-RGBD
공식 코드Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution
https://github.com/chunmeifeng/MINet
공식 코드Multi-Contrast MRI Super-Resolution via a Multi-Stage Integration Network
https://github.com/chunmeifeng/MTrans
공식 코드https://github.com/chunmeifeng/SANet
공식 코드Exploring Separable Attention for Multi-Contrast MR Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.