https://github.com/kunncheng/dit-sr
공식 코드Effective Diffusion Transformer Architecture for Image Super-Resolution
Papers with Code 아카이브
Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 7
- 평가표
- 1
- 보존된 평가 기록
- 1
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/kuty007/multi-kernel-gan
공식 코드https://github.com/ky-zhou/zspu
공식 코드https://github.com/kynthesis/StarSRGAN
공식 코드https://github.com/labforcomputationalvision/local-probability-models-of-images
공식 코드Learning multi-scale local conditional probability models of images
https://github.com/labshuhanggu/adaptive-token-dictionary
공식 코드https://github.com/labshuhanggu/lrti-vsr
공식 코드https://github.com/labshuhanggu/mia-vsr
공식 코드Video Super-Resolution Transformer with Masked Inter&Intra-Frame Attention
https://github.com/labshuhanggu/pft-sr
공식 코드Progressive Focused Transformer for Single Image Super-Resolution
https://github.com/lanha/DSen2
공식 코드Super-resolution of Sentinel-2 images: Learning a globally applicable deep neural network
https://github.com/laoyangui/dlsn
공식 코드Global Learnable Attention for Single Image Super-Resolution
https://github.com/larsOhne/pvdn
공식 코드https://github.com/lbc12345/sed
공식 코드SeD: Semantic-Aware Discriminator for Image Super-Resolution
https://github.com/ldkong1205/robodepth
공식 코드The RoboDepth Challenge: Methods and Advancements Towards Robust Depth Estimation
https://github.com/learninghx/tad-sr
공식 코드One Step Diffusion-based Super-Resolution with Time-Aware Distillation
https://github.com/led-666/ttrd3
공식 코드https://github.com/leejielong/disr-nerf
공식 코드DiSR-NeRF: Diffusion-Guided View-Consistent Super-Resolution NeRF
https://github.com/leiainc/holopix50k
공식 코드https://github.com/lenubolim/textdiff
공식 코드TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution
https://github.com/leon-bungert/blind_remote_sensing
공식 코드Blind Image Fusion for Hyperspectral Imaging with the Directional Total Variation
https://github.com/leonericsson/ensemble4dflownet
공식 코드https://github.com/levente-1/GAMBAS
공식 코드GAMBAS: Generalised-Hilbert Mamba for Super-resolution of Paediatric Ultra-Low-Field MRI
https://github.com/lhaof/robust-sr-against-adversarial-attacks
공식 코드Robust Real-World Image Super-Resolution against Adversarial Attacks
https://github.com/li012589/NeuralWavelet
공식 코드Learning Non-linear Wavelet Transformation via Normalizing Flow
https://github.com/libozhu03/passionsr
공식 코드https://github.com/libozhu03/qartsr
공식 코드https://github.com/lidq92/lsrn-pcgc
공식 코드Lightweight super resolution network for point cloud geometry compression
https://github.com/lidq92/mpeg-pcc-tmc13
공식 코드Hierarchical Prior-based Super Resolution for Point Cloud Geometry Compression
https://github.com/lifengcs/blinddiff
공식 코드BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution
https://github.com/lifengshiwo/DIN
공식 코드Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration
https://github.com/lighting-yxli/tinq
공식 코드TINQ: Temporal Inconsistency Guided Blind Video Quality Assessment
https://github.com/lihenryhfl/diffusion_optimal_control
공식 코드https://github.com/lilygeorgescu/3d-super-res-cnn
공식 코드Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans
https://github.com/lilygeorgescu/mhca
공식 코드https://github.com/linYDTHU/QDM
공식 코드QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution
https://github.com/lincoln20030413/ddsr
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.