https://github.com/chunmeifeng/T2Net
공식 코드Task Transformer Network for Joint MRI Reconstruction and Super-Resolution
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/chxy95/hat
공식 코드Activating More Pixels in Image Super-Resolution Transformer
https://github.com/cicwi/WalnutReconstructionCodes
공식 코드A Cone-Beam X-Ray CT Data Collection designed for Machine Learning
https://github.com/cindydeng1991/Wavelet-Domain-Style-Transfer-for-an-Effective-Perception-distortion-Tradeoff-in-Single-Image-Super-
공식 코드https://github.com/ckkelvinchan/basicvsr_plusplus
공식 코드On the Generalization of BasicVSR++ to Video Deblurring and Denoising
https://github.com/ckkelvinchan/realbasicvsr
공식 코드Investigating Tradeoffs in Real-World Video Super-Resolution
https://github.com/ckyrkou/step_models
공식 코드Toward Efficient Convolutional Neural Networks With Structured Ternary Patterns
https://github.com/claroche-r/dmbsr
공식 코드https://github.com/claroche-r/pnp_ladmm
공식 코드Provably Convergent Plug & Play Linearized ADMM, applied to Deblurring Spatially Varying Kernels
https://github.com/claudiom4sir/stablevsr
공식 코드https://github.com/clovaai/cutblur
공식 코드Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
https://github.com/cmla/PROBAVref
공식 코드Proba-V-ref: Repurposing the Proba-V challenge for reference-aware super resolution
https://github.com/cmlab-korea/awesome-3d-low-level-vision
공식 코드R3eVision: A Survey on Robust Rendering, Restoration, and Enhancement for 3D Low-Level Vision
https://github.com/codeslake/RefVSR
공식 코드Reference-based Video Super-Resolution Using Multi-Camera Video Triplets
https://github.com/codyshen0000/itsrn
공식 코드Implicit Transformer Network for Screen Content Image Continuous Super-Resolution
https://github.com/comp-imaging-sci/mining-tomo-solutions-pulse
공식 코드https://github.com/conor-horgan/DeepeR
공식 코드High-throughput molecular imaging via deep learning enabled Raman spectroscopy
https://github.com/conor-horgan/spectrai
공식 코드https://github.com/coulsonlee/dy-dca-eccv2024
공식 코드Data Overfitting for On-Device Super-Resolution with Dynamic Algorithm and Compiler Co-Design
https://github.com/coulsonlee/stdo-cvpr2023
공식 코드Towards High-Quality and Efficient Video Super-Resolution via Spatial-Temporal Data Overfitting
https://github.com/crcrpar/pytorch.sngan_projection
공식 코드https://github.com/crispyfeso4/anysr
공식 코드AnySR: Realizing Image Super-Resolution as Any-Scale, Any-Resource
https://github.com/cryptonymous9/Augmented-ConvLSTM
공식 코드Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
https://github.com/cs-gangxu/fdan
공식 코드https://github.com/csbhr/Deep-Blind-VSR
공식 코드https://github.com/csbhr/self-blind-vsr
공식 코드https://github.com/csdwren/PReNet
공식 코드Progressive Image Deraining Networks: A Better and Simpler Baseline
https://github.com/csguoh/kd-ltr
공식 코드One-stage Low-resolution Text Recognition with High-resolution Knowledge Transfer
https://github.com/csguoh/lemma
공식 코드Towards Robust Scene Text Image Super-resolution via Explicit Location Enhancement
https://github.com/csguoh/mambair
공식 코드MambaIR: A Simple Baseline for Image Restoration with State-Space Model
https://github.com/csjliang/dasr
공식 코드Efficient and Degradation-Adaptive Network for Real-World Image Super-Resolution
https://github.com/csjliang/ldl
공식 코드Details or Artifacts: A Locally Discriminative Learning Approach to Realistic Image Super-Resolution
https://github.com/csmliu/AdaDSR
공식 코드Deep Adaptive Inference Networks for Single Image Super-Resolution
https://github.com/csslc/ccsr
공식 코드Improving the Stability and Efficiency of Diffusion Models for Content Consistent Super-Resolution
https://github.com/csslc/ea-adam
공식 코드Perception-Distortion Balanced Super-Resolution: A Multi-Objective Optimization Perspective
https://github.com/csslc/pisa-sr
공식 코드Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.