https://github.com/intellabs/mmpano
공식 코드L-MAGIC: Language Model Assisted Generation of Images with Coherence
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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- 보존된 평가 기록
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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/interpolants/couplings
공식 코드https://github.com/inverted-ai/object-detection
공식 코드https://github.com/ioangatop/srVAE
공식 코드https://github.com/iorism/CNN
공식 코드https://github.com/iviplab/cfin
공식 코드Cross-receptive Focused Inference Network for Lightweight Image Super-Resolution
https://github.com/iviplab/ctcnet
공식 코드CTCNet: A CNN-Transformer Cooperation Network for Face Image Super-Resolution
https://github.com/iviplab/fiwhn
공식 코드Efficient Image Super-Resolution with Feature Interaction Weighted Hybrid Network
https://github.com/iviplab/lbnet
공식 코드https://github.com/ivrl/dsr
공식 코드https://github.com/iwuqing/arssr
공식 코드An Arbitrary Scale Super-Resolution Approach for 3D MR Images via Implicit Neural Representation
https://github.com/jacobwulffwold/gan_sr_wind_field
공식 코드https://github.com/jaehakim97/sr4ir
공식 코드Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
https://github.com/jaewon-lee-b/lte
공식 코드Local Texture Estimator for Implicit Representation Function
https://github.com/jaewon-lee-b/ltew
공식 코드Learning Local Implicit Fourier Representation for Image Warping
https://github.com/jaroslaw1007/clit
공식 코드Cascaded Local Implicit Transformer for Arbitrary-Scale Super-Resolution
https://github.com/jasonfu1998/diffpad
공식 코드DiffPAD: Denoising Diffusion-based Adversarial Patch Decontamination
https://github.com/jasonli0707/hklut
공식 코드Hundred-Kilobyte Lookup Tables for Efficient Single-Image Super-Resolution
https://github.com/jbartlett6/sdnet
공식 코드https://github.com/jdfxzzy/dpmn
공식 코드Improving Scene Text Image Super-resolution via Dual Prior Modulation Network
https://github.com/jdfxzzy/pean
공식 코드PEAN: A Diffusion-Based Prior-Enhanced Attention Network for Scene Text Image Super-Resolution
https://github.com/jeonchangbin49/medleyvox
공식 코드MedleyVox: An Evaluation Dataset for Multiple Singing Voices Separation
https://github.com/jerryyann/dpi
공식 코드Diffusion Prior Interpolation for Flexibility Real-World Face Super-Resolution
https://github.com/jhagnberger/vcnef
공식 코드https://github.com/jiahong-fu/kxnet
공식 코드KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution
https://github.com/jiamian-wang/iterative-soft-shrinkage-sr
공식 코드Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution
https://github.com/jiaming-wang/MIP
공식 코드Unsupervised Remote Sensing Super-Resolution via Migration Image Prior
https://github.com/jiangsutx/SPMC_VideoSR
공식 코드https://github.com/jiangxinni/d2a2
공식 코드https://github.com/jianwen-xie/3DDescriptorNet
공식 코드Learning Descriptor Networks for 3D Shape Synthesis and Analysis
https://github.com/jianzeli-114/dfosd
공식 코드Distillation-Free One-Step Diffusion for Real-World Image Super-Resolution
https://github.com/jianzeli-114/fluxsr
공식 코드One Diffusion Step to Real-World Super-Resolution via Flow Trajectory Distillation
https://github.com/jianzhangcs/panini
공식 코드Panini-Net: GAN Prior Based Degradation-Aware Feature Interpolation for Face Restoration
https://github.com/jimmycv07/DiffIR2VR-Zero
공식 코드DiffIR2VR-Zero: Zero-Shot Video Restoration with Diffusion-based Image Restoration Models
https://github.com/jingjin25/LFASR-geometry
공식 코드Learning Light Field Angular Super-Resolution via a Geometry-Aware Network
https://github.com/jingjin25/LFSSR-ATO
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.