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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.
A New Adversarial Perspective for LiDAR-based 3D Object Detection
Sequence Matters: Harnessing Video Models in 3D Super-Resolution
CLIP-SR: Collaborative Linguistic and Image Processing for Super-Resolution
Domain Generalization in Autonomous Driving: Evaluating YOLOv8s, RT-DETR, and YOLO-NAS with the ROAD-Almaty Dataset
Super-Resolution for Remote Sensing Imagery via the Coupling of a Variational Model and Deep Learning
A Single-Frame and Multi-Frame Cascaded Image Super-Resolution Method
Arbitrary-steps Image Super-resolution via Diffusion Inversion
OFTSR: One-Step Flow for Image Super-Resolution with Tunable Fidelity-Realism Trade-offs
RealOSR: Latent Unfolding Boosting Diffusion-based Real-world Omnidirectional Image Super-Resolution
RAP-SR: RestorAtion Prior Enhancement in Diffusion Models for Realistic Image Super-Resolution
MPSI: Mamba enhancement model for pixel-wise sequential interaction Image Super-Resolution
Edge-SD-SR: Low Latency and Parameter Efficient On-device Super-Resolution with Stable Diffusion via Bidirectional Conditioning
Traffic Co-Simulation Framework Empowered by Infrastructure Camera Sensing and Reinforcement Learning
LossAgent: Towards Any Optimization Objectives for Image Processing with LLM Agents
LocalSR: Image Super-Resolution in Local Region
Semantic Segmentation Prior for Diffusion-Based Real-World Super-Resolution
Pixel-level and Semantic-level Adjustable Super-resolution: A Dual-LoRA Approach
RFSR: Improving ISR Diffusion Models via Reward Feedback Learning
TASR: Timestep-Aware Diffusion Model for Image Super-Resolution
HIIF: Hierarchical Encoding based Implicit Image Function for Continuous Super-resolution
CubeFormer: A Simple yet Effective Baseline for Lightweight Image Super-Resolution
Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11
Auto-Encoded Supervision for Perceptual Image Super-Resolution
HAAT: Hybrid Attention Aggregation Transformer for Image Super-Resolution
TSD-SR: One-Step Diffusion with Target Score Distillation for Real-World Image Super-Resolution
Hierarchical Information Flow for Generalized Efficient Image Restoration
HoliSDiP: Image Super-Resolution via Holistic Semantics and Diffusion Prior
FaithDiff: Unleashing Diffusion Priors for Faithful Image Super-resolution
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
MAT: Multi-Range Attention Transformer for Efficient Image Super-Resolution
From Diffusion to Resolution: Leveraging 2D Diffusion Models for 3D Super-Resolution Task
Adversarial Diffusion Compression for Real-World Image Super-Resolution
HF-Diff: High-Frequency Perceptual Loss and Distribution Matching for One-Step Diffusion-Based Image Super-Resolution
Contourlet Refinement Gate Framework for Thermal Spectrum Distribution Regularized Infrared Image Super-Resolution
Zoomed In, Diffused Out: Towards Local Degradation-Aware Multi-Diffusion for Extreme Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.