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Blind Super-Resolution

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하위 태스크: 0
평가표: 18
보존된 평가 기록: 52

Blind Super-Resolution is an image processing technique that aims to reconstruct high-resolution images from low-resolution counterparts without prior knowledge of the degradation process.

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/GuYuanjie/DeepFusionPrior

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Deep Fusion Prior for Plenoptic Super-Resolution All-in-Focus Imaging

https://github.com/IceClear/StableSR

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Exploiting Diffusion Prior for Real-World Image Super-Resolution

https://github.com/JingyunLiang/FKP

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Flow-based Kernel Prior with Application to Blind Super-Resolution

https://github.com/LongguangWang/DASR

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Unsupervised Degradation Representation Learning for Blind Super-Resolution

https://github.com/XYLGroup/DKP

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A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution

https://github.com/YuanfeiHuang/TLSR

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Transitional Learning: Exploring the Transition States of Degradation for Blind Super-resolution

https://github.com/ZhouYiiFeng/CDSR

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Joint Learning Content and Degradation Aware Feature for Blind Super-Resolution

https://github.com/aesrgan/A-ESRGAN

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A-ESRGAN: Training Real-World Blind Super-Resolution with Attention U-Net Discriminators

https://github.com/arcananana/dssr

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Learning Detail-Structure Alternative Optimization for Blind Super-Resolution

https://github.com/berumotto-vermouth/S2R

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S2R: Exploring a Double-Win Transformer-Based Framework for Ideal and Blind Super-Resolution

https://github.com/chaofengc/femasr

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Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors

https://github.com/chaofengc/quantexsr

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Real-World Blind Super-Resolution via Feature Matching with Implicit High-Resolution Priors

https://github.com/greatlog/DAN

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Unfolding the Alternating Optimization for Blind Super Resolution

https://github.com/greatlog/realdan

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End-to-end Alternating Optimization for Real-World Blind Super Resolution

https://github.com/guanghaoyin/CMDSR

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Conditional Hyper-Network for Blind Super-Resolution with Multiple Degradations

https://github.com/hjSim/KOALAnet

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KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment

https://github.com/i2-multimedia-lab/dsat

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Degradation-Aware Self-Attention Based Transformer for Blind Image Super-Resolution

https://github.com/inria-thoth/ddm4ip

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Unsupervised Imaging Inverse Problems with Diffusion Distribution Matching

https://github.com/jiahong-fu/kxnet

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KXNet: A Model-Driven Deep Neural Network for Blind Super-Resolution

https://github.com/junfu1995/sgh

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Scale Guided Hypernetwork for Blind Super-Resolution Image Quality Assessment

https://github.com/kynthesis/StarSRGAN

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StarSRGAN: Improving Real-World Blind Super-Resolution

https://github.com/leon-bungert/blind_remote_sensing

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Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation

https://github.com/lincoln20030413/ddsr

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Dual-Diffusion: Dual Conditional Denoising Diffusion Probabilistic Models for Blind Super-Resolution Reconstruction in RSIs

https://github.com/majedelhelou/SFM

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Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

https://github.com/megvii-research/dcls-sr

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Deep Constrained Least Squares for Blind Image Super-Resolution

https://github.com/pkuliyi2015/sd-webui-stablesr

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Exploiting Diffusion Prior for Real-World Image Super-Resolution

https://github.com/royson/metakernelgan

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Meta-Learned Kernel For Blind Super-Resolution Kernel Estimation

https://github.com/ryanlu2240/boosting-diffusion-guidance-via-learning-degradation-aware-models-for-blind-super-resolution

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Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution

https://github.com/ryanlu2240/dadiff

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Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution

https://github.com/sefibk/KernelGAN

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Blind Super-Resolution Kernel Estimation using an Internal-GAN

https://github.com/sfm-sr-denoising/sfm

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Stochastic Frequency Masking to Improve Super-Resolution and Denoising Networks

https://github.com/shermanlian/kbnet

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Kernel-aware Burst Blind Super-Resolution

https://github.com/sxpro/rfsr

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RFSR: Improving ISR Diffusion Models via Reward Feedback Learning

https://github.com/tencent-ailab/frequency_aug_vae_moesr

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Image Super-resolution Via Latent Diffusion: A Sampling-space Mixture Of Experts And Frequency-augmented Decoder Approach

https://github.com/um-dsrg/rumpy

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The Best of Both Worlds: a Framework for Combining Degradation Prediction with High Performance Super-Resolution Networks

https://github.com/xinntao/Real-ESRGAN

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Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data

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