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/hao1635/lit-former
공식 코드https://github.com/haoheliu/ssr_eval
공식 코드https://github.com/haohou-98/scgan
공식 코드Semi-Cycled Generative Adversarial Networks for Real-World Face Super-Resolution
https://github.com/haonanwang0522/srpose
공식 코드https://github.com/haoyuc/A2N
공식 코드https://github.com/hdspgroup/LD-GAN
공식 코드https://github.com/hdsullivan/ressr
공식 코드ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
https://github.com/healer-ml/msfnet-cpd
공식 코드MSFNet-CPD: Multi-Scale Cross-Modal Fusion Network for Crop Pest Detection
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/hexagonprime/pix2nerf
공식 코드Pix2NeRF: Unsupervised Conditional $π$-GAN for Single Image to Neural Radiance Fields Translation
https://github.com/hey-it-s-me/corple
공식 코드https://github.com/hezongyao/diif
공식 코드Dynamic Implicit Image Function for Efficient Arbitrary-Scale Image Representation
https://github.com/hezongyao/lmf
공식 코드Latent Modulated Function for Computational Optimal Continuous Image Representation
https://github.com/hfates/ikr-net
공식 코드https://github.com/hitrainer/eavsr
공식 코드Benchmark Dataset and Effective Inter-Frame Alignment for Real-World Video Super-Resolution
https://github.com/hjSim/KOALAnet
공식 코드KOALAnet: Blind Super-Resolution using Kernel-Oriented Adaptive Local Adjustment
https://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
https://github.com/hongyuanyu/span
공식 코드Swift Parameter-free Attention Network for Efficient Super-Resolution
https://github.com/house-leo/cfdvsr
공식 코드Collaborative Feedback Discriminative Propagation for Video Super-Resolution
https://github.com/house-leo/ctun
공식 코드Cascaded Temporal Updating Network for Efficient Video Super-Resolution
https://github.com/house-leo/realsr-ccl
공식 코드Criteria Comparative Learning for Real-scene Image Super-Resolution
https://github.com/house-leo/realsr-zero
공식 코드Criteria Comparative Learning for Real-scene Image Super-Resolution
https://github.com/house-leo/rwsr-edl
공식 코드Real-World Image Super-Resolution by Exclusionary Dual-Learning
https://github.com/hqqxyy/msspl
공식 코드https://github.com/huawei-noah/Pretrained-IPT
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.