Deblurring
309 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
RSBlur
REDS
MSU BASED
Rendered WB dataset
Latest papers
Learning Enriched Features via Selective State Spaces Model for Efficient Image Deblurring
The CLGF module is composed of two branches: the global branch captures long-range dependency features via a selective state spaces model, while the local branch employs simplified channel attention to model local connectivity, thereby reducing local pixel forgetting and channel redundancy.
Self-Rectifying Diffusion Sampling with Perturbed-Attention Guidance
KU-CVLAB/Perturbed-Attention-Guidance
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These techniques are often not applicable in unconditional generation or in various downstream tasks such as image restoration.
Omni-Kernel Network for Image Restoration
c-yn/OKNet
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Proceedings of the AAAI Conference on Artificial Intelligence 2024
Extensive experiments demonstrate that our network achieves state-of-the-art performance on 11 benchmark datasets for three representative image restoration tasks, including image dehazing, image desnowing, and image defocus deblurring.
AdaIR: Adaptive All-in-One Image Restoration via Frequency Mining and Modulation
c-yn/adair
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Our approach is motivated by the observation that different degradation types impact the image content on different frequency subbands, thereby requiring different treatments for each restoration task.
DeblurDiNAT: A Lightweight and Effective Transformer for Image Deblurring
To this end, we propose DeblurDiNAT, a compact encoder-decoder Transformer which efficiently restores clean images from real-world blurry ones.
SpikeReveal: Unlocking Temporal Sequences from Real Blurry Inputs with Spike Streams
Our approach begins with the formulation of a spike-guided deblurring model that explores the theoretical relationships among spike streams, blurry images, and their corresponding sharp sequences.
Beyond Text: Frozen Large Language Models in Visual Signal Comprehension
To achieve this, we present the Vision-to-Language Tokenizer, abbreviated as V2T Tokenizer, which transforms an image into a ``foreign language'' with the combined aid of an encoder-decoder, the LLM vocabulary, and a CLIP model.
SMURF: Continuous Dynamics for Motion-Deblurring Radiance Fields
jho-yonsei/smurf
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Neural radiance fields (NeRF) has attracted considerable attention for their exceptional ability in synthesizing novel views with high fidelity.
Dual-domain strip attention for image restoration
c-yn/DSANet
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Neural Networks 2024
In this paper, we develop a dual-domain strip attention mechanism for image restoration by enhancing representation learning, which consists of spatial and frequency strip attention units.
Deep, convergent, unrolled half-quadratic splitting for image deconvolution
6zhc/decun
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Through extensive experimental studies, we verify that our approach achieves competitive performance with state-of-the-art unrolled layer-specific learning and significantly improves over the traditional HQS algorithm.
