Image Defocus Deblurring
27 papers with code • 3 benchmarks • 4 datasets
DPD (Dual-view)
Motion Blurred and Defocused Dataset
SDD
Most implemented papers
Restormer: Efficient Transformer for High-Resolution Image Restoration
Since convolutional neural networks (CNNs) perform well at learning generalizable image priors from large-scale data, these models have been extensively applied to image restoration and related tasks.
Intriguing Findings of Frequency Selection for Image Deblurring
Blur was naturally analyzed in the frequency domain, by estimating the latent sharp image and the blur kernel given a blurry image.
Uformer: A General U-Shaped Transformer for Image Restoration
ZhendongWang6/Uformer
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CVPR 2022
Powered by these two designs, Uformer enjoys a high capability for capturing both local and global dependencies for image restoration.
Improving Image Restoration by Revisiting Global Information Aggregation
Our TLC converts global operations to local ones only during inference so that they aggregate features within local spatial regions rather than the entire large images.
Learning Single Image Defocus Deblurring with Misaligned Training Pairs
liyucs/jdrl
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First, in the deblurring module, a bi-directional optical flow-based deformation is introduced to tolerate spatial misalignment between deblurred and ground-truth images.
Defocus Deblurring Using Dual-Pixel Data
Abdullah-Abuolaim/defocus-deblurring-dual-pixel
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ECCV 2020
DP sensors are used to assist a camera's auto-focus by capturing two sub-aperture views of the scene in a single image shot.
Learning to Reduce Defocus Blur by Realistically Modeling Dual-Pixel Data
Abdullah-Abuolaim/recurrent-defocus-deblurring-synth-dual-pixel
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ICCV 2021
Leveraging these realistic synthetic DP images, we introduce a recurrent convolutional network (RCN) architecture that improves deblurring results and is suitable for use with single-frame and multi-frame data (e. g., video) captured by DP sensors.
Attention! Stay Focus!
tuvovan/ATTSF
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We develop a deep convolutional neural networks(CNNs) to deal with the blurry artifacts caused by the defocus of the camera using dual-pixel images.
BaMBNet: A Blur-aware Multi-branch Network for Defocus Deblurring
In particular, we estimate the blur amounts of different regions by the internal geometric constraint of the DP data, which measures the defocus disparity between the left and right views.
Improving Single-Image Defocus Deblurring: How Dual-Pixel Images Help Through Multi-Task Learning
Abdullah-Abuolaim/multi-task-defocus-deblurring-dual-pixel-nimat
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Specifically, we show that jointly learning to predict the two DP views from a single blurry input image improves the network's ability to learn to deblur the image.
