Image Defocus Deblurring
25 papers with code • 3 benchmarks • 4 datasets
DPD (Dual-view)
SDD
Motion Blurred and Defocused Dataset
Most implemented papers
Single Image Defocus Deblurring Using Kernel-Sharing Parallel Atrous Convolutions
HyeongseokSon1/KPAC
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ICCV 2021
To utilize the property with inverse kernels, we exploit the observation that when only the size of a defocus blur changes while keeping the shape, the shape of the corresponding inverse kernel remains the same and only the scale changes.
Iterative Filter Adaptive Network for Single Image Defocus Deblurring
We propose a novel end-to-end learning-based approach for single image defocus deblurring.
Gaussian Kernel Mixture Network for Single Image Defocus Deblurring
cszcwu/gkmnet
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NeurIPS 2021
Defocus blur is one kind of blur effects often seen in images, which is challenging to remove due to its spatially variant amount.
Learning to Deblur using Light Field Generated and Real Defocus Images
lingyanruan/DRBNet
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CVPR 2022
We first train the network on a light field-generated dataset for its highly accurate image correspondence.
Focal Network for Image Restoration
Image restoration aims to reconstruct a sharp image from its degraded counterpart, which plays an important role in many fields.
Single Image Defocus Deblurring via Implicit Neural Inverse Kernels
xinyao240/INIKNet
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ICCV 2023
Single image defocus deblurring (SIDD) is a challenging task due to the spatially-varying nature of defocus blur, characterized by per-pixel point spread functions (PSFs).
Revisiting Image Deblurring with an Efficient ConvNet
In this work, we propose a unified lightweight CNN network that features a large effective receptive field (ERF) and demonstrates comparable or even better performance than Transformers while bearing less computational costs.
Efficient and Explicit Modelling of Image Hierarchies for Image Restoration
ofsoundof/grl-image-restoration
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CVPR 2023
The aim of this paper is to propose a mechanism to efficiently and explicitly model image hierarchies in the global, regional, and local range for image restoration.
Masked Autoencoders as Image Processors
Recently, masked autoencoders (MAE) for feature pre-training have further unleashed the potential of Transformers, leading to state-of-the-art performances on various high-level vision tasks.
Selective Frequency Network for Image Restoration
c-yn/SFNet
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Conference 2023
Image restoration aims to reconstruct the latent sharp image from its corrupted counterpart.
