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Image Super-Resolution
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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.
SatDiffMoE: A Mixture of Estimation Method for Satellite Image Super-resolution with Latent Diffusion Models
Blind Super-Resolution via Meta-learning and Markov Chain Monte Carlo Simulation
SR-CACO-2: A Dataset for Confocal Fluorescence Microscopy Image Super-Resolution
One-Step Effective Diffusion Network for Real-World Image Super-Resolution
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
Towards Realistic Data Generation for Real-World Super-Resolution
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
Binarized Diffusion Model for Image Super-Resolution
Arctic Sea Ice Image Super-Resolution Based on Multi-Scale Convolution and Dual-Gating Mechanism
Improving Object Detector Training on Synthetic Data by Starting With a Strong Baseline Methodology
Principled Probabilistic Imaging using Diffusion Models as Plug-and-Play Priors
Single image super-resolution based on trainable feature matching attention network
PatchScaler: An Efficient Patch-Independent Diffusion Model for Image Super-Resolution
Does Diffusion Beat GAN in Image Super Resolution?
Looks Too Good To Be True: An Information-Theoretic Analysis of Hallucinations in Generative Restoration Models
BOLD: Boolean Logic Deep Learning
Fast-DDPM: Fast Denoising Diffusion Probabilistic Models for Medical Image-to-Image Generation
Universal Robustness via Median Randomized Smoothing for Real-World Super-Resolution
Traffic control using intelligent timing of traffic lights with reinforcement learning technique and real-time processing of surveillance camera images
Perceptual Fairness in Image Restoration
Bangladeshi Native Vehicle Detection in Wild
Infrared Image Super-Resolution via Lightweight Information Split Network
IRSRMamba: Infrared Image Super-Resolution via Mamba-based Wavelet Transform Feature Modulation Model
Frequency-Domain Refinement with Multiscale Diffusion for Super Resolution
RSHazeDiff: A Unified Fourier-aware Diffusion Model for Remote Sensing Image Dehazing
Large coordinate kernel attention network for lightweight image super-resolution
Perception- and Fidelity-aware Reduced-Reference Super-Resolution Image Quality Assessment
Multimodal Collaboration Networks for Geospatial Vehicle Detection in Dense, Occluded, and Large-Scale Events
NAFRSSR: a Lightweight Recursive Network for Efficient Stereo Image Super-Resolution
CDFormer:When Degradation Prediction Embraces Diffusion Model for Blind Image Super-Resolution
Exploring the Low-Pass Filtering Behavior in Image Super-Resolution
Efficient Real-world Image Super-Resolution Via Adaptive Directional Gradient Convolution
Semantic Guided Large Scale Factor Remote Sensing Image Super-resolution with Generative Diffusion Prior
Multi-Level Feature Fusion Network for Lightweight Stereo Image Super-Resolution
Frequency-Assisted Mamba for Remote Sensing Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.