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No-Reference Image Quality Assessment
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An Image Quality Assessment approach where no reference image information is available to the model. Sometimes referred to as Blind Image Quality Assessment (BIQA).
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
DGIQA: Depth-guided Feature Attention and Refinement for Generalizable Image Quality Assessment
Taylor expansion-based Kolmogorov-Arnold network for blind image quality assessment
VisualQuality-R1: Reasoning-Induced Image Quality Assessment via Reinforcement Learning to Rank
Semantically-Aware Game Image Quality Assessment
Exploring Semantic Feature Discrimination for Perceptual Image Super-Resolution and Opinion-Unaware No-Reference Image Quality Assessment
Cross-Domain Underwater Image Enhancement Guided by No-Reference Image Quality Assessment: A Transfer Learning Approach
IQPFR: An Image Quality Prior for Blind Face Restoration and Beyond
Computational Analysis of Degradation Modeling in Blind Panoramic Image Quality Assessment
Light Field Image Quality Assessment With Auxiliary Learning Based on Depthwise and Anglewise Separable Convolutions
Backdoor Attacks against No-Reference Image Quality Assessment Models via a Scalable Trigger
A No-Reference Medical Image Quality Assessment Method Based on Automated Distortion Recognition Technology: Application to Preprocessing in MRI-guided Radiotherapy
Dual-Representation Interaction Driven Image Quality Assessment with Restoration Assistance
Stochastic BIQA: Median Randomized Smoothing for Certified Blind Image Quality Assessment
SEAGULL: No-reference Image Quality Assessment for Regions of Interest via Vision-Language Instruction Tuning
Visual question answering based evaluation metrics for text-to-image generation
Scale Contrastive Learning with Selective Attentions for Blind Image Quality Assessment
A Novel Automatic Real-time Motion Tracking Method for Magnetic Resonance Imaging-guided Radiotherapy: Leveraging the Enhanced Tracking-Learning-Detection Framework with Automatic Segmentation
Multi-task Feature Enhancement Network for No-Reference Image Quality Assessment
AIM 2024 Challenge on UHD Blind Photo Quality Assessment
Attention Down-Sampling Transformer, Relative Ranking and Self-Consistency for Blind Image Quality Assessment
ExIQA: Explainable Image Quality Assessment Using Distortion Attributes
MobileIQA: Exploiting Mobile-level Diverse Opinion Network For No-Reference Image Quality Assessment Using Knowledge Distillation
LAR-IQA: A Lightweight, Accurate, and Robust No-Reference Image Quality Assessment Model
A Deep-Learning-Based Label-free No-Reference Image Quality Assessment Metric: Application in Sodium MRI Denoising
MSLIQA: Enhancing Learning Representations for Image Quality Assessment through Multi-Scale Learning
No-Reference Image Quality Assessment with Global-Local Progressive Integration and Semantic-Aligned Quality Transfer
GSBIQA: Green Saliency-guided Blind Image Quality Assessment Method
DSMix: Distortion-Induced Sensitivity Map Based Pre-training for No-Reference Image Quality Assessment
Local Manifold Learning for No-Reference Image Quality Assessment
UHD-IQA Benchmark Database: Pushing the Boundaries of Blind Photo Quality Assessment
DDR: Exploiting Deep Degradation Response as Flexible Image Descriptor
GenzIQA: Generalized Image Quality Assessment using Prompt-Guided Latent Diffusion Models
Rank-based No-reference Quality Assessment for Face Swapping
ATTIQA: Generalizable Image Quality Feature Extractor using Attribute-aware Pretraining
DP-IQA: Utilizing Diffusion Prior for Blind Image Quality Assessment in the Wild
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