Papers with Code 아카이브
Grayscale Image Denoising
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.
Hierarchical Information Flow for Generalized Efficient Image Restoration
Fast and Interpretable Nonlocal Neural Networks for Image Denoising via Group-Sparse Convolutional Dictionary Learning
SwinIA: Self-Supervised Blind-Spot Image Denoising without Convolutions
KBNet: Kernel Basis Network for Image Restoration
Adversarial Distortion Learning for Medical Image Denoising
Improving Image Restoration by Revisiting Global Information Aggregation
Restormer: Efficient Transformer for High-Resolution Image Restoration
SwinIR: Image Restoration Using Swin Transformer
CDLNet: Robust and Interpretable Denoising Through Deep Convolutional Dictionary Learning
LIDIA: Lightweight Learned Image Denoising with Instance Adaptation
Image Restoration Using Deep Regulated Convolutional Networks
Index Network
Deep Graph-Convolutional Image Denoising
Blind Universal Bayesian Image Denoising with Gaussian Noise Level Learning
Real Image Denoising with Feature Attention
Residual Dense Network for Image Restoration
Neural Nearest Neighbors Networks
Non-Local Recurrent Network for Image Restoration
Multi-level Wavelet-CNN for Image Restoration
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
Neural Affine Grayscale Image Denoising
Learning Deep CNN Denoiser Prior for Image Restoration
Beyond a Gaussian Denoiser: Residual Learning of Deep CNN for Image Denoising
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
Trainable Nonlinear Reaction Diffusion: A Flexible Framework for Fast and Effective Image Restoration
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.