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.
IM-LUT: Interpolation Mixing Look-Up Tables for Image Super-Resolution
Unsupervised Image Super-Resolution Reconstruction Based on Real-World Degradation Patterns
Efficient Feedback Gate Network for Hyperspectral Image Super-Resolution
Fiber Signal Denoising Algorithm using Hybrid Deep Learning Networks
Efficient Star Distillation Attention Network for Lightweight Image Super-Resolution
On the Natural Robustness of Vision-Language Models Against Visual Perception Attacks in Autonomous Driving
Structural Similarity-Inspired Unfolding for Lightweight Image Super-Resolution
Stroke-based Cyclic Amplifier: Image Super-Resolution at Arbitrary Ultra-Large Scales
Incorporating Uncertainty-Guided and Top-k Codebook Matching for Real-World Blind Image Super-Resolution
Task-driven real-world super-resolution of document scans
Enhancing Frequency for Single Image Super-Resolution with Learnable Separable Kernels
Text-Aware Real-World Image Super-Resolution via Diffusion Model with Joint Segmentation Decoders
Multi-scale Image Super Resolution with a Single Auto-Regressive Model
DACN: Dual-Attention Convolutional Network for Hyperspectral Image Super-Resolution
Practical Manipulation Model for Robust Deepfake Detection
A Tree-guided CNN for image super-resolution
Application of convolutional neural networks in image super-resolution
Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
SeG-SR: Integrating Semantic Knowledge into Remote Sensing Image Super-Resolution via Vision-Language Model
TextSR: Diffusion Super-Resolution with Multilingual OCR Guidance
Advancing Image Super-resolution Techniques in Remote Sensing: A Comprehensive Survey
VME: A Satellite Imagery Dataset and Benchmark for Detecting Vehicles in the Middle East and Beyond
DiMoSR: Feature Modulation via Multi-Branch Dilated Convolutions for Efficient Image Super-Resolution
Burst Image Super-Resolution via Multi-Cross Attention Encoding and Multi-Scan State-Space Decoding
Chain-of-Zoom: Extreme Super-Resolution via Scale Autoregression and Preference Alignment
BadSR: Stealthy Label Backdoor Attacks on Image Super-Resolution
Every Pixel Tells a Story: End-to-End Urdu Newspaper OCR
Trustworthy Image Super-Resolution via Generative Pseudoinverse
CLIP-aware Domain-Adaptive Super-Resolution
HSRMamba: Efficient Wavelet Stripe State Space Model for Hyperspectral Image Super-Resolution
ORL-LDM: Offline Reinforcement Learning Guided Latent Diffusion Model Super-Resolution Reconstruction
Super-Resolution Generative Adversarial Networks based Video Enhancement
Joint Low-level and High-level Textual Representation Learning with Multiple Masking Strategies
High-Frequency Prior-Driven Adaptive Masking for Accelerating Image Super-Resolution
Semantic-Guided Diffusion Model for Single-Step Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.