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Image Super-Resolution
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Image Super-Resolution is a machine learning task where the goal is to increase the resolution of an image, often by a factor of 4x or more, while maintaining its content and details as much as possible. The end result is a high-resolution version of the original image. This task can be used for various applications such as improving image quality, enhancing visual detail, and increasing the accuracy of computer vision algorithms.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/genforce/mganprior
공식 코드https://github.com/george-gca/sr-pytorch-lightning
공식 코드Single Image Super-Resolution Based on Capsule Neural Networks
https://github.com/gfxdisp/colorvideovdp
공식 코드Training Neural Networks on RAW and HDR Images for Restoration Tasks
https://github.com/gladzhang/art
공식 코드Accurate Image Restoration with Attention Retractable Transformer
https://github.com/goddesshs/transmrsr
공식 코드TransMRSR: Transformer-based Self-Distilled Generative Prior for Brain MRI Super-Resolution
https://github.com/gohtanii/DiverSeg-dataset
공식 코드Rethinking Image Super-Resolution from Training Data Perspectives
https://github.com/google-research/lag
공식 코드Creating High Resolution Images with a Latent Adversarial Generator
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/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/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/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/guyuanjie/deep-retinex-fusion
공식 코드Physics Driven Deep Retinex Fusion for Adaptive Infrared and Visible Image Fusion
https://github.com/h-deep/trans-svsr
공식 코드A New Dataset and Transformer for Stereoscopic Video Super-Resolution
https://github.com/hadipardis/svc
공식 코드https://github.com/hanlinwu/sadn
공식 코드Scale-Aware Dynamic Network for Continuous-Scale Super-Resolution
https://github.com/haoyuc/A2N
공식 코드https://github.com/hdsullivan/ressr
공식 코드ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
https://github.com/hellloxiaotian/ACNet
공식 코드https://github.com/hellloxiaotian/LESRCNN
공식 코드https://github.com/hellloxiaotian/adsrnet
공식 코드Adaptive Convolutional Neural Network for Image Super-resolution
https://github.com/hellloxiaotian/dsrnet
공식 코드https://github.com/hellloxiaotian/esrgcnn
공식 코드Image Super-resolution with An Enhanced Group Convolutional Neural Network
https://github.com/hellloxiaotian/hdsrnet
공식 코드Adaptive Convolutional Neural Network for Image Super-resolution
https://github.com/hellloxiaotian/hgsrcnn
공식 코드https://github.com/hellloxiaotian/tsrnet
공식 코드https://github.com/hengliusky/UltraSound_SSSR
공식 코드Perception Consistency Ultrasound Image Super-resolution via Self-supervised CycleGAN
https://github.com/herrinj/LAP
공식 코드LAP: a Linearize and Project Method for Solving Inverse Problems with Coupled Variables
https://github.com/hey-it-s-me/corple
공식 코드https://github.com/hfates/ikr-net
공식 코드https://github.com/hkzhang91/hinas
공식 코드Memory-Efficient Hierarchical Neural Architecture Search for Image Restoration
https://github.com/hms97/evd4uav
공식 코드EVD4UAV: An Altitude-Sensitive Benchmark to Evade Vehicle Detection in UAV
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.