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Image Denoising
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Image Denoising is a computer vision task that involves removing noise from an image. Noise can be introduced into an image during acquisition or processing, and can reduce image quality and make it difficult to interpret. Image denoising techniques aim to restore an image to its original quality by reducing or removing the noise, while preserving the important features of the image.
<span style="color:grey; opacity: 0.6">( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
EdiBERT, a generative model for image editing
IDR: Self-Supervised Image Denoising via Iterative Data Refinement
Unsupervised Image Denoising with Frequency Domain Knowledge
Image denoising by Super Neurons: Why go deep?
ISNAS-DIP: Image-Specific Neural Architecture Search for Deep Image Prior
Deep neural networks-based denoising models for CT imaging and their efficacy
Restormer: Efficient Transformer for High-Resolution Image Restoration
Discriminative Dictionary Learning based on Statistical Methods
Image Denoising in FPGA using Generic Risk Estimation
SDnDTI: Self-supervised deep learning-based denoising for diffusion tensor MRI
Moment Transform-Based Compressive Sensing in Image Processing
Hyperspectral Mixed Noise Removal via Subspace Representation and Weighted Low-rank Tensor Regularization
Graph-Based Depth Denoising & Dequantization for Point Cloud Enhancement
Survey of Deep Learning Methods for Inverse Problems
Self-Verification in Image Denoising
Functional Neural Networks for Parametric Image Restoration Problems
As if by magic: self-supervised training of deep despeckling networks with MERLIN
Generative Flows as a General Purpose Solution for Inverse Problems
Exploring Gradient Flow Based Saliency for DNN Model Compression
Dynamic Slimmable Denoising Network
Rethinking Noise Synthesis and Modeling in Raw Denoising
Preconditioned Plug-and-Play ADMM with Locally Adjustable Denoiser for Image Restoration
Towards Flexible Blind JPEG Artifacts Removal
Targeted Gradient Descent: A Novel Method for Convolutional Neural Networks Fine-tuning and Online-learning
Hyperparameter selection for Discrete Mumford-Shah
Learning-based Noise Component Map Estimation for Image Denoising
From Simulated to Visual Data: A Robust Low-Rank Tensor Completion Approach using lp-Regression for Outlier Resistance
Eformer: Edge Enhancement based Transformer for Medical Image Denoising
WINNet: Wavelet-inspired Invertible Network for Image Denoising
Dynamic Attentive Graph Learning for Image Restoration
A Complex Constrained Total Variation Image Denoising Algorithm with Application to Phase Retrieval
Anatomical-Guided Attention Enhances Unsupervised PET Image Denoising Performance
Image Denoising Inspired by Quantum Many-Body physics
Noise2Fast: Fast Self-Supervised Single Image Blind Denoising
SwinIR: Image Restoration Using Swin Transformer
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.