A fast preview restoration algorithm for space-variant degraded images

Victor Karnaukhov; Vitaly Kober

doi:10.1117/12.2236812

28 September 2016 A fast preview restoration algorithm for space-variant degraded images

Victor Karnaukhov, Vitaly Kober

Proceedings Volume 9971, Applications of Digital Image Processing XXXIX; 99712X (2016) https://doi.org/10.1117/12.2236812
Event: SPIE Optical Engineering + Applications, 2016, San Diego, California, United States

Abstract

The paper deals with restoration of decimated images degraded by space-variant distortions. Such distortions occur in real conditions when the camera in an actual shoot is shaken and rotated in three dimensions while its shutter is open. The proposed method is locally adaptive image restoration in the domain of a sliding orthogonal transform. It is assumed that the signal distortion operator is spatially homogeneous in a small sliding window. A fast preview restoration algorithm for degraded images is proposed. To achieve the image restoration with low resolution at high rate, a fast recursive algorithm for computing the sliding discrete cosine transform with arbitrary step is utilized. The proposed algorithm is tested with spatially nonuniform distortion operators and obtained results are discussed.

Citation Download Citation

Victor Karnaukhov and Vitaly Kober "A fast preview restoration algorithm for space-variant degraded images", Proc. SPIE 9971, Applications of Digital Image Processing XXXIX, 99712X (28 September 2016); https://doi.org/10.1117/12.2236812

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available