https://github.com/maum-ai/nuwave
NU-Wave: A Diffusion Probabilistic Model for Neural Audio Upsampling
Papers with Code 아카이브
Super-Resolution
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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/maum-ai/nuwave2
NU-Wave 2: A General Neural Audio Upsampling Model for Various Sampling Rates
https://github.com/mayank-17/Project-Image-Restoration-using-SRCNN
https://github.com/mayuelala/followyourpose
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mbarbetti/tf-gen-models
https://github.com/mcbuehler/DeepSEE
DeepSEE: Deep Disentangled Semantic Explorative Extreme Super-Resolution
https://github.com/mcordts/cityscapesScripts
Cityscapes 3D: Dataset and Benchmark for 9 DoF Vehicle Detection
https://github.com/mdjsos/meidejin
https://github.com/med-seg/kidney
https://github.com/med-seg/kidney-mc
https://github.com/media2x/realsr-ncnn-vulkan-python
NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
https://github.com/mediatek-neuropilot/mai22-real-time-video-sr
Deploying Image Deblurring across Mobile Devices: A Perspective of Quality and Latency
https://github.com/medical-image-analysis-laboratory/fetal_brain_qc
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
https://github.com/medical-image-analysis-laboratory/fetalsynthseg
Brain-ID: Learning Contrast-agnostic Anatomical Representations for Brain Imaging
https://github.com/medical-image-analysis-laboratory/fetmrqc
Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction
https://github.com/megvii-research/TLC
https://github.com/megvii-research/tlsc
https://github.com/mehrhardt/blind_remote_sensing
Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
https://github.com/meitalB/NN
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
https://github.com/mengxiangming/qcs-sgm
Diffusion Model Based Posterior Sampling for Noisy Linear Inverse Problems
https://github.com/michael13162/DoodleGAN
https://github.com/michaeltrs/DeepSatData
Context-self contrastive pretraining for crop type semantic segmentation
https://github.com/micmic123/qmapcompression
Recovering Realistic Texture in Image Super-resolution by Deep Spatial Feature Transform
https://github.com/microsoft/visual-chatgpt
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/mike9251/DAQ
DAQ: Channel-Wise Distribution-Aware Quantization for Deep Image Super-Resolution Networks
https://github.com/mileyan/pseudo-LiDAR_e2e
https://github.com/milmor/latent-diffusion-transformer
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/milmor/perceptual-losses-neural-style
Perceptual Losses for Real-Time Style Transfer and Super-Resolution
https://github.com/mindspore-ai/contrib/tree/master/intern/pixelshuffle1d
https://github.com/mindspore-ai/contrib/tree/master/papers/CSD
Towards Compact Single Image Super-Resolution via Contrastive Self-distillation
https://github.com/mindspore-ai/models/blob/master/research/cv/EDSR
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/mindspore-ai/models/blob/master/research/cv/gan
https://github.com/mindspore-ai/models/blob/master/research/cv/wdsr
Enhanced Deep Residual Networks for Single Image Super-Resolution
https://github.com/mindspore-ai/models/tree/master/official/cv/RDN
https://github.com/mindspore-ai/models/tree/master/official/cv/dncnn
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
https://github.com/mindspore-ai/models/tree/master/official/cv/srcnn
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.