2020
DOI: 10.3390/computers9020030
|View full text |Cite
|
Sign up to set email alerts
|

Eliminating Nonuniform Smearing and Suppressing the Gibbs Effect on Reconstructed Images

Abstract: In this work, the problem of eliminating a nonuniform rectilinear smearing of an image is considered, using a mathematical- and computer-based approach. An example of such a problem is a picture of several cars, moving with different speeds, taken with a fixed camera. The problem is described by a set of one-dimensional Fredholm integral equations (IEs) of the first kind of convolution type, with a one-dimensional point spread function (PSF) when uniform smearing, and by a set of new one-dimensional IEs of a g… Show more

Help me understand this report
View preprint versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2

Citation Types

0
0
0

Year Published

2023
2023
2024
2024

Publication Types

Select...
1
1

Relationship

0
2

Authors

Journals

citations
Cited by 2 publications
(2 citation statements)
references
References 17 publications
0
0
0
Order By: Relevance
“…On the other hand, frequency domain denoising 3/3 algorithms can effectively eliminate noise from remote sensing images while maintaining edge and texture information. Nevertheless, they also introduce ringing effects caused by the Gibbs phenomenon, significantly impairing the visual quality of images 7,8 . Moreover, these traditional methods typically necessitate manual parameter adjustments to accommodate different types of noise and image characteristics, restricting their flexibility and adaptability 9 .…”
Section: Introductionmentioning
confidence: 99%
“…On the other hand, frequency domain denoising 3/3 algorithms can effectively eliminate noise from remote sensing images while maintaining edge and texture information. Nevertheless, they also introduce ringing effects caused by the Gibbs phenomenon, significantly impairing the visual quality of images 7,8 . Moreover, these traditional methods typically necessitate manual parameter adjustments to accommodate different types of noise and image characteristics, restricting their flexibility and adaptability 9 .…”
Section: Introductionmentioning
confidence: 99%
“…В [12] (см. также [13,14,15]) для восстановления параметра дефокусировки используется приближенная модель передаточной функции и проводится сравнение распределения ее нулей с нулями энергетического спектра размытого изображения. В [16] использовалось ядро в форме гауссиана для оценки параметра размытия на основе изучения распределения границ и анализа их размытия с помощью отношения модулей градиентов (см.…”
Section: Introductionunclassified