Abstract
AbstractGiven an image filter $${{\varvec{y}}}={{\varvec{f}}}\,({{\varvec{x}}})$$
y
=
f
(
x
)
, where $${{\varvec{x}}}$$
x
and $${{\varvec{y}}}$$
y
are input and output images, respectively, reverse image filtering consists of rendering an approximation to $${{\varvec{x}}}$$
x
from $${{\varvec{y}}}$$
y
using the filter $${{\varvec{f}}}\,(\cdot )$$
f
(
·
)
itself as a black box, without knowing the internal structure of the filter. In this paper, we propose to use modified Landweber iterations for reverse image filtering, evaluate the performance of our approach, and present applications to image deblurring and super-resolution. An important advantage of our approach over the existing reverse image filtering methods is high robustness to noise.
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Reference35 articles.
1. Anger, J., Facciolo, G., Delbracio, M.: Blind image deblurring using the l0 gradient prior. Image Process. Line 9, 124–142 (2019)
2. Belyaev, A.G., Fayolle, P.A.: Two iterative methods for reverse image filtering. Signal. Image. Video. Process. 15, 1565–1573 (2021)
3. Bertero, M., Boccacci, P.: Introduction to inverse problems in imaging. IOP Publishing (1998)
4. Chan, S.H., Khoshabeh, R., Gibson, K.B., Gill, P.E., Nguyen, T.Q.: An augmented Lagrangian method for total variation video restoration. IEEE Trans. Image Process. 20(11), 3097–111 (2011)
5. Chao, D., Chen, C.L., He, K., Tang, X.: Learning a deep convolutional network for image super-resolution. In: ECCV (2014)
Cited by
8 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献