Results 81 to 90 of about 4,335 (202)

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

Image Deblurring and Effect of Wiener, Regularized, Lucky-Richardson, Blind-Deconvolution, Average and Median Filters on Blurred Image and Noisy Image

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  

Efficient Adjoint Computation for Wavelet and Convolution Operators

, 2016
First-order optimization algorithms, often preferred for large problems, require the gradient of the differentiable terms in the objective function. These gradients often involve linear operators and their adjoints, which must be applied rapidly.
Becker, Stephen, Folberth, James
core   +1 more source

Efficient, blind, spatially-variant deblurring for shaken images [PDF]

, 2014
In this chapter we discuss modeling and removing spatially-variant blur from photographs. We describe a compact global parameterization of camera shake blur, based on the 3D rotation of the camera during the exposure. Our model uses three-parameter homographies to connect camera motion to image motion and, by assigning weights to a set of these ...
Whyte, Oliver, Sivic, Josef, Zisserman, Andrew, Ponce, Jean   +3 more
openaire   +2 more sources

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, Li Cheng, Zhichang Guo, Yuming Xing   +3 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, Kun Gao, Xiaodian Zhang, Hong Wang   +3 more
doaj   +1 more source

Automatic Estimation of Modulation Transfer Functions

, 2018
The modulation transfer function (MTF) is widely used to characterise the performance of optical systems. Measuring it is costly and it is thus rarely available for a given lens specimen.
Bauer, Matthias, Hirsch, Michael, Schölkopf, Bernhard, Volchkov, Valentin   +3 more
core   +1 more source

A generative adversarial network approach for removing motion blur in the automatic detection of pavement cracks

Computer-Aided Civil and Infrastructure Engineering, Volume 39, Issue 22, Page 3412-3434, 15 November 2024.
Abstract Advancements in infrastructure management have significantly benefited from automatic pavement crack detection systems, relying on image processing enhanced by high‐resolution imaging and machine learning. However, image and motion blur substantially challenge the accuracy of crack detection and analysis.
Yu Zhang, Lin Zhang
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  

Blind Image Deblurring Using Row–Column Sparse Representations

IEEE Signal Processing Letters, 2018
Blind image deblurring is a particularly challenging inverse problem where the blur kernel is unknown and must be estimated en route to recover the deblurred image. The problem is of strong practical relevance since many imaging devices such as cellphone cameras, must rely on deblurring algorithms to yield satisfactory image quality.
Mohammad Tofighi, Yuelong Li, Vishal Monga   +2 more
openaire   +2 more sources