Joint learning of motion deblurring and defocus deblurring networks with a real-world dataset

Y Li, X Shu, D Ren, Q Li, W Zuo - Neurocomputing, 2024 - Elsevier
When recovering the sharp image from a blurry observation, moving objects, eg, people and
vehicles, usually attract more perceptual attention. However, existing motion deblurring …

Defocus blur detection based on transformer and complementary residual learning

S Chai, X Zhao, J Zhang, J Kan - Multimedia Tools and Applications, 2024 - Springer
Defocus blur detection (DBD), a technique for detecting defocus or in-focus pixels in a single
image, has been widely used in various fields. Although deep learning-based methods …

Defocus blur detection via adaptive cross-level feature fusion and refinement

Z Zhao, H Yang, P Liu, H Nie, Z Zhang, C Li - The Visual Computer, 2024 - Springer
Convolutional neural networks have achieved competitive performance in defocus blur
detection (DBD). However, due to the different receptive fields of different convolutional …

Swin-Diff: a single defocus image deblurring network based on diffusion model

H Liang, S Chai, X Zhao, J Kan - Complex & Intelligent Systems, 2025 - Springer
Abstract Single Image Defocus Deblurring (SIDD) remains challenging due to spatially
varying blur kernels, particularly in processing high-resolution images where traditional …

Efficient image blur detection via hierarchical edge guidance and region complementation

X Wang, X Liang, S Li, J Zheng - Complex & Intelligent Systems, 2023 - Springer
Blur detection is aimed to recognize the blurry pixels from a given image, which is
increasingly valued in vision-centered applications. Albeit great improvement achieved by …

[PDF][PDF] Street view images blur detection

M Moeini, E Yaghoubi, S Frintrop - Journal of Autonomous …, 2024 - inf.uni-hamburg.de
Blurred regions in images can hinder visual analysis and have a notable impact on
applications such as navigation systems and virtual tours. Many existing approaches in the …