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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.
Deep Learning based Optical Image Super-Resolution via Generative Diffusion Models for Layerwise in-situ LPBF Monitoring
Single-Layer Learnable Activation for Implicit Neural Representation (SL$^{2}$A-INR)
NSSR-DIL: Null-Shot Image Super-Resolution Using Deep Identity Learning
WaveMixSR-V2: Enhancing Super-resolution with Higher Efficiency
Learning Two-factor Representation for Magnetic Resonance Image Super-resolution
Test-time Training for Hyperspectral Image Super-resolution
Lightweight single-image super-resolution network based on dual paths
EigenSR: Eigenimage-Bridged Pre-Trained RGB Learners for Single Hyperspectral Image Super-Resolution
Perceptual-Distortion Balanced Image Super-Resolution is a Multi-Objective Optimization Problem
LMLT: Low-to-high Multi-Level Vision Transformer for Image Super-Resolution
Solving Video Inverse Problems Using Image Diffusion Models
SeCo-INR: Semantically Conditioned Implicit Neural Representations for Improved Medical Image Super-Resolution
Rethinking Image Super-Resolution from Training Data Perspectives
HiTSR: A Hierarchical Transformer for Reference-based Super-Resolution
Histo-Diffusion: A Diffusion Super-Resolution Method for Digital Pathology with Comprehensive Quality Assessment
VFM-Det: Towards High-Performance Vehicle Detection via Large Foundation Models
ResSR: A Computationally Efficient Residual Approach to Super-Resolving Multispectral Images
Comparing YOLOv5 Variants for Vehicle Detection: A Performance Analysis
3D Photon Counting CT Image Super-Resolution Using Conditional Diffusion Model
MambaCSR: Dual-Interleaved Scanning for Compressed Image Super-Resolution With SSMs
Implicit Grid Convolution for Multi-Scale Image Super-Resolution
ML-CrAIST: Multi-scale Low-high Frequency Information-based Cross black Attention with Image Super-resolving Transformer
QMambaBSR: Burst Image Super-Resolution with Query State Space Model
Task-Aware Dynamic Transformer for Efficient Arbitrary-Scale Image Super-Resolution
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
GRFormer: Grouped Residual Self-Attention for Lightweight Single Image Super-Resolution
DIffSteISR: Harnessing Diffusion Prior for Superior Real-world Stereo Image Super-Resolution
SSL: A Self-similarity Loss for Improving Generative Image Super-resolution
Content-decoupled Contrastive Learning-based Implicit Degradation Modeling for Blind Image Super-Resolution
Kalman-Inspired Feature Propagation for Video Face Super-Resolution
Efficient Single Image Super-Resolution with Entropy Attention and Receptive Field Augmentation
Image Super-Resolution with Taylor Expansion Approximation and Large Field Reception
MEVDT: Multi-Modal Event-Based Vehicle Detection and Tracking Dataset
Sewer Image Super-Resolution with Depth Priors and Its Lightweight Network
GaussianSR: High Fidelity 2D Gaussian Splatting for Arbitrary-Scale Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.