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/Mind23-2/MindCode-5/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/Mind23-2/MindCode-5/tree/main/RDN
https://github.com/Mind23-2/MindCode-5/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/Mind23-2/MindCode-78
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Mind23-2/MindCode-81
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/Mind23-2/MindCode-88/tree/main/SinGAN
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/Mind23-2/MindCode-94
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/MindCode-4/code-10/tree/main/CSD
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/MindCode-4/code-5/tree/main/swin2sr
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/MindSpore-MS-Code2/code0/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/MindSpore-paper-code-2/code2/tree/main/RCAN
Revisiting RCAN: Improved Training for Image Super-Resolution
https://github.com/MindSpore-paper-code-2/code2/tree/main/RDN
https://github.com/MindSpore-paper-code-2/code2/tree/main/rbpn
Recurrent Back-Projection Network for Video Super-Resolution
https://github.com/MindSpore-paper-code-2/code3/tree/main/wdsr
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/MindSpore-paper-code-3/code10/tree/main/GhostSR
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
https://github.com/MindSpore-paper-code-3/code2/tree/main/CSNL
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/MindSpore-paper-code-3/code4/tree/main/GhostSR
GhostSR: Learning Ghost Features for Efficient Image Super-Resolution
https://github.com/MindSpore-paper-code-3/code4/tree/main/HiFaceGAN
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
https://github.com/MindSpore-paper-code-3/code6/tree/main/csd
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/MindSpore-paper-code-3/code8/tree/main/CSNL
Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network
https://github.com/MindSpore-paper-code-3/code8/tree/main/DBPN
Deep Back-Projection Networks for Single Image Super-resolution
https://github.com/MindSpore-paper-code-3/code8/tree/main/DnCNN
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/MindSpore-paper-code-3/code8/tree/main/esr_ea
Efficient Residual Dense Block Search for Image Super-Resolution
https://github.com/MindSpore-paper-code-3/code9/tree/main/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/MindSpore-scientific-2/code-10/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MindSpore-scientific-2/code-10/tree/main/Super-Resolution-for-Root-Imaging
LMR: A Large-Scale Multi-Reference Dataset for Reference-based Super-Resolution
https://github.com/MindSpore-scientific-2/code-10/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific-2/code-11/tree/main/Super-Resolution-for-Root-Imaging
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/MindSpore-scientific-2/code-8/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-1/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-10/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-4/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific/code-8/tree/main/SING
SinGAN: Learning a Generative Model from a Single Natural Image
https://github.com/MohammedAlkhrashi/4D-Dream
Accelerating the Super-Resolution Convolutional Neural Network
https://github.com/MohammedAlkhrashi/TMA
Accelerating the Super-Resolution Convolutional Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.