Abstract

In this paper we propose an approach for handling noise in deconvolution algorithm based on multidirectional filters. Most image deconvolution techniques are sensitive to the noise. Even a small amount of noise will degrade the quality of image estimation dramatically. We found that by applying a directional low-pass filter to the blurred image, we can reduce the noise level while preserving the blur information in the orthogonal direction to the filter. So we apply a series of directional filters at different orientations to the blurred image, and a guided filter based edge-preserving image deconvolution is used to estimate an accurate Radon transform of the clear image from each filtered image. Finally, we reconstruct the original image using the inverse Radon transform. We compare our deconvolution algorithm with many competitive deconvolution techniques in terms of the improvement in signal-to-noise ratio and visual quality.

© 2013 Optical Society of America

Full Article  |  PDF Article
More Like This
Dictionary learning approach for image deconvolution with variance estimation

Hang Yang, Ming Zhu, Xiaotian Wu, Zhongbo Zhang, and Heyan Huang
Appl. Opt. 53(25) 5677-5684 (2014)

Satellite image deconvolution based on nonlocal means

Ming Zhao, Wei Zhang, Zhile Wang, and Qingyu Hou
Appl. Opt. 49(32) 6286-6294 (2010)

Blind deconvolution of a noisy degraded image

Jianlin Zhang, Qiheng Zhang, and Guangming He
Appl. Opt. 48(12) 2350-2355 (2009)

References

You do not have subscription access to this journal. Citation lists with outbound citation links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Cited By

You do not have subscription access to this journal. Cited by links are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Figures (7)

You do not have subscription access to this journal. Figure files are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Tables (3)

You do not have subscription access to this journal. Article tables are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Equations (18)

You do not have subscription access to this journal. Equations are available to subscribers only. You may subscribe either as an Optica member, or as an authorized user of your institution.

Contact your librarian or system administrator
or
Login to access Optica Member Subscription

Metrics