Deblurring
313 papers with code • 15 benchmarks • 14 datasets
Deblurring is a computer vision task that involves removing the blurring artifacts from images or videos to restore the original, sharp content. Blurring can be caused by various factors such as camera shake, fast motion, and out-of-focus objects, and can result in a loss of detail and quality in the captured images. The goal of deblurring is to produce a clear, high-quality image that accurately represents the original scene.
( Image credit: Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks )
Benchmarks
These leaderboards are used to track progress in Deblurring
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Libraries
Use these libraries to find Deblurring models and implementationsDatasets
GoPro
Real Blur Dataset
HIDE
Dialogue State Tracking Challenge
Deblur-NeRF
RSBlur
REDS
MSU BASED
Rendered WB dataset
Latest papers with no code
GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration
The network is a simple shallow network with an efficient block that implements global additive multidimensional averaging operations.
IPT-V2: Efficient Image Processing Transformer using Hierarchical Attentions
Recent advances have demonstrated the powerful capability of transformer architecture in image restoration.
Distilling Semantic Priors from SAM to Efficient Image Restoration Models
SPD leverages a self-distillation manner to distill the fused semantic priors to boost the performance of original IR models.
Blur2Blur: Blur Conversion for Unsupervised Image Deblurring on Unknown Domains
This algorithm works by transforming a blurry input image, which is challenging to deblur, into another blurry image that is more amenable to deblurring.
Boosting Image Restoration via Priors from Pre-trained Models
Pre-trained models with large-scale training data, such as CLIP and Stable Diffusion, have demonstrated remarkable performance in various high-level computer vision tasks such as image understanding and generation from language descriptions.
Decoupled Data Consistency with Diffusion Purification for Image Restoration
To solve image restoration problems, many existing techniques achieve data consistency by incorporating additional likelihood gradient steps into the reverse sampling process of diffusion models.
Learning to Deblur Polarized Images
However, since the on-chip micro-polarizers block part of the light so that the sensor often requires a longer exposure time, the captured polarized images are prone to motion blur caused by camera shakes, leading to noticeable degradation in the computed DoP and AoP.
Passive Snapshot Coded Aperture Dual-Pixel RGB-D Imaging
Our resulting CADS imaging system demonstrates improvement of >1. 5dB PSNR in all-in-focus (AIF) estimates and 5-6% in depth estimation quality over naive DP sensing for a wide range of aperture settings.
ARIN: Adaptive Resampling and Instance Normalization for Robust Blind Inpainting of Dunhuang Cave Paintings
While state-of-the-art deep neural networks show impressive results for image enhancement, they often struggle to enhance real-world images.
IRConStyle: Image Restoration Framework Using Contrastive Learning and Style Transfer
By leveraging the flexibility of ConStyle, we develop a \textbf{general restoration network} for image restoration.
