Image Denoising
413 papers with code • 19 benchmarks • 17 datasets
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.
( Image credit: Wide Inference Network for Image Denoising via Learning Pixel-distribution Prior )
Benchmarks
These leaderboards are used to track progress in Image Denoising
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Libraries
Use these libraries to find Image Denoising models and implementations
FFHQ
BSD
Urban100
SIDD
SID
CBSD68
PolyU
DND
CRVD
ELD
Most implemented papers
CycleISP: Real Image Restoration via Improved Data Synthesis
This is mainly because the AWGN is not adequate for modeling the real camera noise which is signal-dependent and heavily transformed by the camera imaging pipeline.
Multi-Stage Progressive Image Restoration
At each stage, we introduce a novel per-pixel adaptive design that leverages in-situ supervised attention to reweight the local features.
FFDNet: Toward a Fast and Flexible Solution for CNN based Image Denoising
cszn/FFDNet
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Due to the fast inference and good performance, discriminative learning methods have been widely studied in image denoising.
Noise2Void - Learning Denoising from Single Noisy Images
juglab/n2v
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CVPR 2019
The field of image denoising is currently dominated by discriminative deep learning methods that are trained on pairs of noisy input and clean target images.
Index Network
poppinace/indexnet_matting
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By viewing the indices as a function of the feature map, we introduce the concept of "learning to index", and present a novel index-guided encoder-decoder framework where indices are self-learned adaptively from data and are used to guide the downsampling and upsampling stages, without extra training supervision.
Multi-level Wavelet-CNN for Image Restoration
With the modified U-Net architecture, wavelet transform is introduced to reduce the size of feature maps in the contracting subnetwork.
HiFaceGAN: Face Renovation via Collaborative Suppression and Replenishment
Existing face restoration researches typically relies on either the degradation prior or explicit guidance labels for training, which often results in limited generalization ability over real-world images with heterogeneous degradations and rich background contents.
A Multi-Head Convolutional Neural Network With Multi-path Attention improves Image Denoising
jiahongz/mhcnn
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Unlike previous attention mechanisms that handle pixel-level, channel-level, or patch-level features, MPA focuses on features at the image level.
Recurrent Inference Machines for Solving Inverse Problems
Much of the recent research on solving iterative inference problems focuses on moving away from hand-chosen inference algorithms and towards learned inference.
Unprocessing Images for Learned Raw Denoising
google-research/google-research
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CVPR 2019
Machine learning techniques work best when the data used for training resembles the data used for evaluation.
