Papers with Code 아카이브

Image Super-Resolution

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법 전체 데이터셋

하위 태스크: 5
평가표: 69
보존된 평가 기록: 748

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/knazeri/edge-informed-sisr

공식 코드

Edge-Informed Single Image Super-Resolution

https://github.com/korouuuuu/hma

공식 코드

HMANet: Hybrid Multi-Axis Aggregation Network for Image Super-Resolution

https://github.com/krenerd/ultimate-sr

공식 코드

One-to-many Approach for Improving Super-Resolution

https://github.com/kritiksoman/GIMP-ML

공식 코드

GIMP-ML: Python Plugins for using Computer Vision Models in GIMP

https://github.com/kuis-ai-tekalp-research-group/perceptual-vsr

공식 코드

Spatio-Temporal Perception-Distortion Trade-off in Learned Video SR

https://github.com/kumar-shridhar/PyTorch-BayesianCNN

공식 코드

A Comprehensive guide to Bayesian Convolutional Neural Network with Variational Inference

https://github.com/kunncheng/dit-sr

공식 코드

Effective Diffusion Transformer Architecture for Image Super-Resolution

https://github.com/kynthesis/StarSRGAN

공식 코드

StarSRGAN: Improving Real-World Blind Super-Resolution

https://github.com/labshuhanggu/adaptive-token-dictionary

공식 코드

Transcending the Limit of Local Window: Advanced Super-Resolution Transformer with Adaptive Token Dictionary

https://github.com/labshuhanggu/lrti-vsr

공식 코드

Small Clips, Big Gains: Learning Long-Range Refocused Temporal Information for Video Super-Resolution

https://github.com/labshuhanggu/pft-sr

공식 코드

Progressive Focused Transformer for Single Image Super-Resolution

https://github.com/laoyangui/dlsn

공식 코드

Global Learnable Attention for Single Image Super-Resolution

https://github.com/larsOhne/pvdn

공식 코드

Provident Vehicle Detection at Night: The PVDN Dataset

https://github.com/lbc12345/sed

공식 코드

SeD: Semantic-Aware Discriminator for Image Super-Resolution

https://github.com/learninghx/tad-sr

공식 코드

One Step Diffusion-based Super-Resolution with Time-Aware Distillation

https://github.com/led-666/ttrd3

공식 코드

TTRD3: Texture Transfer Residual Denoising Dual Diffusion Model for Remote Sensing Image Super-Resolution

https://github.com/lenubolim/textdiff

공식 코드

TextDiff: Mask-Guided Residual Diffusion Models for Scene Text Image Super-Resolution

https://github.com/lhaof/robust-sr-against-adversarial-attacks

공식 코드

Robust Real-World Image Super-Resolution against Adversarial Attacks

https://github.com/libozhu03/passionsr

공식 코드

PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution

https://github.com/libozhu03/qartsr

공식 코드

QArtSR: Quantization via Reverse-Module and Timestep-Retraining in One-Step Diffusion based Image Super-Resolution

https://github.com/lifengcs/blinddiff

공식 코드

BlindDiff: Empowering Degradation Modelling in Diffusion Models for Blind Image Super-Resolution

https://github.com/lifengshiwo/DIN

공식 코드

Learning Deep Interleaved Networks with Asymmetric Co-Attention for Image Restoration

https://github.com/lilygeorgescu/3d-super-res-cnn

공식 코드

Convolutional Neural Networks with Intermediate Loss for 3D Super-Resolution of CT and MRI Scans

https://github.com/lilygeorgescu/mhca

공식 코드

Multimodal Multi-Head Convolutional Attention with Various Kernel Sizes for Medical Image Super-Resolution

https://github.com/linYDTHU/QDM

공식 코드

QDM: Quadtree-Based Region-Adaptive Sparse Diffusion Models for Efficient Image Super-Resolution

https://github.com/liqilei/GMFN

공식 코드

Gated Multiple Feedback Network for Image Super-Resolution

https://github.com/littlebeen/asddpm-adaptive-semantic-enhanced-ddpm

공식 코드

Adaptive Semantic-Enhanced Denoising Diffusion Probabilistic Model for Remote Sensing Image Super-Resolution

https://github.com/littlepure2333/ape

공식 코드

Adaptive Patch Exiting for Scalable Single Image Super-Resolution

https://github.com/littlepure2333/samplingaug

공식 코드

SamplingAug: On the Importance of Patch Sampling Augmentation for Single Image Super-Resolution

https://github.com/liu-zihang/samsr

공식 코드

Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution

https://github.com/liuguandu/rc-lut

공식 코드

Reconstructed Convolution Module Based Look-Up Tables for Efficient Image Super-Resolution

https://github.com/liuofficial/sdp

공식 코드

A Spectral Diffusion Prior for Hyperspectral Image Super-Resolution

https://github.com/lixinustc/awesome-diffusion-model-for-image-processing

공식 코드

Diffusion Models for Image Restoration and Enhancement -- A Comprehensive Survey

https://github.com/lixinustc/kvqe-challenge-cvpr-ntire2025

공식 코드

NTIRE 2025 Challenge on Short-form UGC Video Quality Assessment and Enhancement: Methods and Results

https://github.com/liyuantsao/BFSR

공식 코드

Boosting Flow-based Generative Super-Resolution Models via Learned Prior

https://github.com/liyuantsao/HoliSDiP

공식 코드

HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.