Papers with Code 아카이브
Image Super-Resolution
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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.
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
An Advanced Features Extraction Module for Remote Sensing Image Super-Resolution
Residual-Conditioned Optimal Transport: Towards Structure-Preserving Unpaired and Paired Image Restoration
DVMSR: Distillated Vision Mamba for Efficient Super-Resolution
Single Image Super-Resolution Based on Global-Local Information Synergy
Detail-Enhancing Framework for Reference-Based Image Super-Resolution
Swin2-MoSE: A New Single Image Super-Resolution Model for Remote Sensing
Federated Learning for Blind Image Super-Resolution
Cross-sensor super-resolution of irregularly sampled Sentinel-2 time series
Real-Time 4K Super-Resolution of Compressed AVIF Images. AIS 2024 Challenge Survey
Deep learning-based blind image super-resolution with iterative kernel reconstruction and noise estimation
A Dynamic Kernel Prior Model for Unsupervised Blind Image Super-Resolution
A Survey on Visual Mamba
Deep RAW Image Super-Resolution. A NTIRE 2024 Challenge Survey
SwinFuSR: an image fusion-inspired model for RGB-guided thermal image super-resolution
SEGSRNet for Stereo-Endoscopic Image Super-Resolution and Surgical Instrument Segmentation
Single-sample image-fusion upsampling of fluorescence lifetime images
Partial Large Kernel CNNs for Efficient Super-Resolution
Training Transformer Models by Wavelet Losses Improves Quantitative and Visual Performance in Single Image Super-Resolution
OmniSSR: Zero-shot Omnidirectional Image Super-Resolution using Stable Diffusion Model
The Ninth NTIRE 2024 Efficient Super-Resolution Challenge Report
Efficient Conditional Diffusion Model with Probability Flow Sampling for Image Super-resolution
MTKD: Multi-Teacher Knowledge Distillation for Image Super-Resolution
NTIRE 2024 Challenge on Image Super-Resolution ($\times$4): Methods and Results
Differentiable Search for Finding Optimal Quantization Strategy
LIPT: Latency-aware Image Processing Transformer
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Efficient Learnable Collaborative Attention for Single Image Super-Resolution
AdaBM: On-the-Fly Adaptive Bit Mapping for Image Super-Resolution
Knowledge Distillation with Multi-granularity Mixture of Priors for Image Super-Resolution
RefQSR: Reference-based Quantization for Image Super-Resolution Networks
Beyond Image Super-Resolution for Image Recognition with Task-Driven Perceptual Loss
DeeDSR: Towards Real-World Image Super-Resolution via Degradation-Aware Stable Diffusion
DRCT: Saving Image Super-resolution away from Information Bottleneck
Exploiting Self-Supervised Constraints in Image Super-Resolution
Super-Resolution of SOHO/MDI Magnetograms of Solar Active Regions Using SDO/HMI Data and an Attention-Aided Convolutional Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.