Deblurring
312 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
Motion-adaptive Separable Collaborative Filters for Blind Motion Deblurring
In particular, we use a motion estimation network to capture motion information from neighborhoods, thereby adaptively estimating spatially-variant motion flow, mask, kernels, weights, and offsets to obtain the MISC Filter.
Real-World Efficient Blind Motion Deblurring via Blur Pixel Discretization
As recent advances in mobile camera technology have enabled the capability to capture high-resolution images, such as 4K images, the demand for an efficient deblurring model handling large motion has increased.
DeblurGS: Gaussian Splatting for Camera Motion Blur
Although significant progress has been made in reconstructing sharp 3D scenes from motion-blurred images, a transition to real-world applications remains challenging.
Real-world Instance-specific Image Goal Navigation for Service Robots: Bridging the Domain Gap with Contrastive Learning
To address this, we propose a novel method called Few-shot Cross-quality Instance-aware Adaptation (CrossIA), which employs contrastive learning with an instance classifier to align features between massive low- and few high-quality images.
Mansformer: Efficient Transformer of Mixed Attention for Image Deblurring and Beyond
By elaborate adjustment of the tensor shapes and dimensions for the dot product, we split the typical self-attention of quadratic complexity into four operations of linear complexity.
HDR Imaging for Dynamic Scenes with Events
High dynamic range imaging (HDRI) for real-world dynamic scenes is challenging because moving objects may lead to hybrid degradation of low dynamic range and motion blur.
Specularity Factorization for Low-Light Enhancement
We present a new additive image factorization technique that treats images to be composed of multiple latent specular components which can be simply estimated recursively by modulating the sparsity during decomposition.
Gyro-based Neural Single Image Deblurring
To handle gyro error, GyroDeblurNet is equipped with two novel neural network blocks: a gyro refinement block and a gyro deblurring block.
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.
