https://github.com/gongyeliu/ssd
공식 코드Accelerating Diffusion Models for Inverse Problems through Shortcut Sampling
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/goodfeli/adversarial
공식 코드https://github.com/google-research/google-research
공식 코드https://github.com/google-research/google-research/tree/master/comisr
공식 코드https://github.com/google-research/lag
공식 코드Creating High Resolution Images with a Latent Adversarial Generator
https://github.com/google-research/population-dynamics
공식 코드General Geospatial Inference with a Population Dynamics Foundation Model
https://github.com/google/zoom-to-inpaint
공식 코드Zoom-to-Inpaint: Image Inpainting with High-Frequency Details
https://github.com/greatlog/DAN
공식 코드Unfolding the Alternating Optimization for Blind Super Resolution
https://github.com/greatlog/UnpairedSR
공식 코드Learning the Degradation Distribution for Blind Image Super-Resolution
https://github.com/greatlog/realdan
공식 코드End-to-end Alternating Optimization for Real-World Blind Super Resolution
https://github.com/gregongie/super_inrs
공식 코드Sampling Theory for Super-Resolution with Implicit Neural Representations
https://github.com/grigorisg9gr/polynomial_nets_for_conditional_generation
공식 코드CoPE: Conditional image generation using Polynomial Expansions
https://github.com/grishavak/Patch_Ordering_as_a_Regularization_for_Inverse_Problems_in_Image_Processing
공식 코드Patch-Ordering as a Regularization for Inverse Problems in Image Processing
https://github.com/grovessss/hfh-font
공식 코드HFH-Font: Few-shot Chinese Font Synthesis with Higher Quality, Faster Speed, and Higher Resolution
https://github.com/guaishou74851/adcsr
공식 코드Adversarial Diffusion Compression for Real-World Image Super-Resolution
https://github.com/guanghaoyin/CMDSR
공식 코드Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations
https://github.com/guangyuankk/diffmsr
공식 코드Rethinking Diffusion Model for Multi-Contrast MRI Super-Resolution
https://github.com/guhuangai/ddm-public
공식 코드Simultaneous Image-to-Zero and Zero-to-Noise: Diffusion Models with Analytical Image Attenuation
https://github.com/guochengqian/TENet
공식 코드Rethinking Learning-based Demosaicing, Denoising, and Super-Resolution Pipeline
https://github.com/guoyaoshen/k-sapcecolddiffusion
공식 코드Learning to Reconstruct Accelerated MRI Through K-space Cold Diffusion without Noise
https://github.com/guoyaoshen/rnst-regularization_by_nst
공식 코드Regularization by Neural Style Transfer for MRI Field-Transfer Reconstruction with Limited Data
https://github.com/guoyongcs/DRN
공식 코드Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
https://github.com/guoyongcs/HNAS-SR
공식 코드Hierarchical Neural Architecture Search for Single Image Super-Resolution
https://github.com/guoyongcs/drc
공식 코드Towards Lightweight Super-Resolution with Dual Regression Learning
https://github.com/guthasaibharathchandra/MAP-GA
공식 코드Inverse Problems with Diffusion Models: A MAP Estimation Perspective
https://github.com/guyuanjie/deep-retinex-fusion
공식 코드Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion
https://github.com/gyguo/pixeldistillation
공식 코드Pixel Distillation: A New Knowledge Distillation Scheme for Low-Resolution Image Recognition
https://github.com/h-deep/trans-svsr
공식 코드A New Dataset and Transformer for Stereoscopic Video Super-Resolution
https://github.com/hadidaneshmand/measure_recovery_codes
공식 코드Polynomial-time Sparse Measure Recovery: From Mean Field Theory to Algorithm Design
https://github.com/hadipardis/svc
공식 코드https://github.com/hahnec/rf-ulm
공식 코드RF-ULM: Ultrasound Localization Microscopy Learned from Radio-Frequency Wavefronts
https://github.com/hahnec/stofnet
공식 코드https://github.com/hamadichihaoui/BIRD
공식 코드https://github.com/handsomewzy/HSI-DMGASR
공식 코드Enhancing Hyperspectral Images via Diffusion Model and Group-Autoencoder Super-resolution Network
https://github.com/hanlinwu/E2DiffSR
공식 코드https://github.com/hanlinwu/sadn
공식 코드Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.