Results 81 to 90 of about 9,413,043 (217)
Data-driven gradient priors integrated into blind image deblurring
Blind image deblurring is a severely ill-posed task. Most existing methods focus on deep learning to learn massive data features while ignoring the vital significance of classic image structure priors.
Li, Chongyi, Guo, Jichang, Qing, Qi
core +1 more source
Tikrit Journal of Engineering Sciences, 2014 In image deblurring, we try to recover the original, sharp image by using a mathematical model of the blurring process. There are several techniques to recover the original image, but they could not recover the image exactly.
Shayma Wail Nourildean Mohammed Ismaeel Khalil
doaj
A blind deblurring and image decomposition approach for astronomical image restoration [PDF]
2015 23rd European Signal Processing Conference (EUSIPCO), 2015 With the progress of adaptive optics systems, ground-based telescopes acquire images with improved resolutions. However, compensation for atmospheric turbulence is still partial, which leaves good scope for digital restoration techniques to recover fine details in the images.
Mourya, Rahul +3 more
openaire +3 more sources
Multi‐Scale Frequency Enhancement Network for Blind Image Deblurring
IET Image Processing, Volume 19, Issue 1, January/December 2025.We propose a multi‐scale frequency enhancement network (MFENet) for blind image deblurring, which integrates multi‐scale feature extraction and frequency enhancement to recover fine details. MFENet includes a multi‐scale feature extraction module (MS‐FE) and a frequency enhanced blur perception module (FEBP) to improve performance on deblurring ...
YaWen Xiang +5 more
wiley +1 more source
More Realistic Edges, Textures, and Colors for Image Non‐Homogeneous Dehazing
IET Image Processing, Volume 19, Issue 1, January/December 2025.The study proposes an image dehazing method to improve performance in non‐homogeneous and/or dense haze scenarios, ensuring high texture detail and color fidelity in dehazed images.The proposed method employs a multi‐scale encoder–decoder structure to effectively capture finer edge and texture details.
Hairu Guo +4 more
wiley +1 more source
Fast Blind Image Deblurring Using Smoothing-Enhancing Regularizer
IEEE Access, 2019 Blind deconvolution is a highly ill-posed problem for the restoration of degraded images and requires prior knowledge or regularization. Recently, various priors have been proposed and the models based on these priors have achieved state-of-the-art ...
Zeyang Dou +3 more
doaj +1 more source
FCUnet: An Underwater Image Enhancement Hybrid Network via Fused Feature‐Guided Cross‐Attention
IET Image Processing, Volume 19, Issue 1, January/December 2025.This paper proposes a hybrid CNN‐transformer network for enhancing underwater images. Our approach integrated cross‐attention into the U‐shaped structure with fused feature guidance, designing a colour deviation preprocessing module, a feature fusion unit and a multi‐term loss function to enhance feature extraction capability and adaptability of the ...
Jie Zhu +4 more
wiley +1 more source
Efficient Dark Channel Prior Based Blind Image De-blurring [PDF]
Radioengineering, 2021 Dark channel prior for blind image de-blurring has attained considerable attention in recent past. An interesting observation in blurring process is that the value of dark channel increases after averaging with adjacent high intensity pixels.
J. Ahmad, I. Touqir, A. M. Siddiqui
doaj
Neural‐network‐based regularization methods for inverse problems in imaging
GAMM-Mitteilungen, Volume 47, Issue 4, November 2024.Abstract This review provides an introduction to—and overview of—the current state of the art in neural‐network based regularization methods for inverse problems in imaging. It aims to introduce readers with a solid knowledge in applied mathematics and a basic understanding of neural networks to different concepts of applying neural networks for ...
Andreas Habring, Martin Holler
wiley +1 more source
Deep learning informed diffusion equation model for image denoising
IET Image Processing, Volume 18, Issue 13, Page 4310-4327, 13 November 2024.The paper presents a Deep Learning Informed Diffusion Equation (DLI‐DE) framework for image denoising, which integrates CNN‐derived image priors into diffusion equations to avoid artifacts common with conventional CNN methods. The uniqueness of the DLI‐DE solution ensures artifact‐free and high‐quality denoising, with performance comparable to advanced
Yao Li +3 more
wiley +1 more source