Results 91 to 100 of about 293,019 (211)
Blind Deblurring via a Novel Recursive Deep CNN Improved by Wavelet Transform
IEEE Access, 2018 Blind image deconvolution is an ill-posed problem, which is mainly addressed by the regularization methods. Wavelet transform is an effective denoising method related to regularized inversion. In this paper, wavelet transform is utilized to decompose and
Chao Min +3 more
doaj +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
Image Deblurring Method with Fractional-order Total Variation and Adaptive Regularization Parameters
工程科学与技术, 2018 In order to better recovery texture details of images, avoid the difficulty of selecting the regularization parameters when solving the image deblurring model, a novel non-blind image deblurring method by using fractional order TV (FOTV) and adaptive ...
Xiaomei YANG +3 more
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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
Bayesian deblurring with integrated noise estimation
, 2022 S.2625-2632Conventional non-blind image deblurring algorithms involve natural image priors and maximum a-posteriori (MAP) estimation. As a consequence of MAP estimation, separate pre-processing steps such as noise estimation and training of the ...
Schmidt, Uwe +2 more
core +1 more source
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
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
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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