https://github.com/nSamsow/CSNLN-new
Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
Papers with Code 아카이브
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/nabarunbaruaAIML/ATCC_Yolov5
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/nagadomi/waifu2x
https://github.com/nannau/DoWnGAN
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/nekitmm/starnet
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/nhatsmrt/superres
Deep Laplacian Pyramid Networks for Fast and Accurate Super-Resolution
https://github.com/niazwazir/REAL_TIME_VIDEO_IMSR
https://github.com/nihui/realsr-ncnn-vulkan
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/nikhilsu/SuperPixel
High-Quality Face Image SR Using Conditional Generative Adversarial Networks
https://github.com/nikunj-taneja/accendo
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/ninatu/mood_challenge
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/nitaifingerhut/SinGAN-for-Object-Removal
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/nmhkahn/CARN-pytorch
Fast, Accurate, and Lightweight Super-Resolution with Cascading Residual Network
https://github.com/nonslowrunner/GAN-Upscale-Image
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/noufali/VideoML
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/noyomer/few-shots-generation
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/npielawski/pytorch_tiramisu
https://github.com/nqian1/survey-on-odisr-and-odvsr
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-Resolution
https://github.com/nunof98/AiVision_Anomaly_Detection
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/ofsoundof/Heterogeneity_Hypothesis
Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/ofsoundof/group_sparsity
Learning Filter Basis for Convolutional Neural Network Compression
https://github.com/olaviinha/NeuralImageSuperResolution
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/omerb01/puq
https://github.com/omermosa/Single-Image-Colorization-and-Super-Resolution
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/omkarghugarkar007/Neural_Super_Sampling
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/oneTaken/pytorch_fast_style_transfer
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/open-mmlab/mmgeneration
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/opteroncx/SESR
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
https://github.com/pankajb64/bayescnn
A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference
https://github.com/pbehjatii/OverNet
Hierarchical Residual Attention Network for Single Image Super-Resolution
https://github.com/peteryuX/esrgan-tf2
ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks
https://github.com/pilot7747/sldl
Designing a Practical Degradation Model for Deep Blind Image Super-Resolution
https://github.com/pminhtam/DeepBurstDenoising
https://github.com/pminhtam/MWCNN
https://github.com/pmm09c/ntire-dehazing
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/pohwa065/Deep-Learning-for-Super-Resolution-and-Image-Enhancement
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.