https://github.com/Mind23-2/MindCode-94
Enhanced Deep Residual Networks for Single Image Super-Resolution
Papers with Code 아카이브
3D Object Super-Resolution
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3D object super-resolution is the task of up-sampling 3D objects.
<span style="color:grey; opacity: 0.6">( Image credit: Multi-View Silhouette and Depth Decomposition for High Resolution 3D Object Representation )</span>
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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-11/tree/main/DCTransformer
https://github.com/MindCode-4/code-5/tree/main/swin2sr
Swin2SR: SwinV2 Transformer for Compressed Image Super-Resolution and Restoration
https://github.com/MindCode-4/code-8/tree/main/Projected-Distribution
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/code10/tree/main/gan
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/code2/tree/main/DBPN
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-11/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/MindSpore-scientific-2/code-3/tree/main/stable-sam
https://github.com/MindSpore-scientific-2/code-4/tree/main/pixelshuffle1d
PixelShuffler: A Simple Image Translation Through Pixel Rearrangement
https://github.com/MindSpore-scientific-2/code-5/tree/main/Least_Generative
https://github.com/MindSpore-scientific-2/code-8/tree/main/Super-Resolution-for-Root-Imaging
https://github.com/MindSpore-scientific-2/code-8/tree/main/pixelshuffle1d
https://github.com/MindSpore-scientific-2/code-9/tree/main/Least_Generative
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-11/tree/main/Super-Resolution-for-Root-Imaging
High-Resolution Image Synthesis with Latent Diffusion Models
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.