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JPEG Artifact Removal
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Fractal-IR: A Unified Framework for Efficient and Scalable Image Restoration
Compression-Aware One-Step Diffusion Model for JPEG Artifact Removal
Hierarchical Information Flow for Generalized Efficient Image Restoration
JDEC: JPEG Decoding via Enhanced Continuous Cosine Coefficients
Bidirectional Consistency Models
Lightweight Adaptive Feature De-drifting for Compressed Image Classification
Controlling Vision-Language Models for Multi-Task Image Restoration
JPEG Quantized Coefficient Recovery via DCT Domain Spatial-Frequential Transformer
Restore Anything Pipeline: Segment Anything Meets Image Restoration
Removing Image Artifacts From Scratched Lens Protectors
DriftRec: Adapting diffusion models to blind JPEG restoration
Hypernetwork-Based Adaptive Image Restoration
Towards Flexible Blind JPEG Artifacts Removal
Training a Task-Specific Image Reconstruction Loss
Projected Distribution Loss for Image Enhancement
Are Deep Neural Architectures Losing Information? Invertibility Is Indispensable
Restoring Images with Unknown Degradation Factors by Recurrent Use of a Multi-branch Network
DMCNN: Dual-Domain Multi-Scale Convolutional Neural Network for Compression Artifacts Removal
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.