Computationally scalable adaptive image interpolation algorithm using maximum-likelihood denoising for real-time applications

Kwok-Wai Hung; Wan-Chi Siu

doi:10.1117/1.JEI.22.4.043006

9 October 2013 Computationally scalable adaptive image interpolation algorithm using maximum-likelihood denoising for real-time applications

Kwok-Wai Hung, Wan-Chi Siu

Author Affiliations +

Journal of Electronic Imaging, Vol. 22, Issue 4, 043006 (October 2013). https://doi.org/10.1117/1.JEI.22.4.043006

Abstract

Computationally scalable image interpolation algorithm is always desirable for software and hardware implementations on center processing unit (CPU), digital signal processor, field-programmable gate array, and low-cost hardware. A low-complexity, computationally scalable, and data-adaptive image interpolation algorithm that has a simple and homogeneous structure to efficiently scale the computation is proposed. Specifically, the image interpolation as a denoising problem is formulated by proposing a new image model to relate the observed low-resolution pixels and missing high-resolution pixels. Applying the maximum-likelihood estimation using the new image model results in an adaptive linear filter, where the filter coefficients depend on the local noise covariance matrix, which is estimated by local noise samples. Due to low overhead of the proposed interpolator, the overall computation efficiently scales with the number of noise samples. Experimental results show that the proposed scalable algorithm outperforms the state-of-the-art fast algorithms and achieves more than 36 frames per second for upscaling a 540 p (960×540 ) video to a 1080 p (1920×1080 ) video using multithreaded C++ software implementation on a PC system with Intel i7 950 3 GHz CPU.

Citation Download Citation

Kwok-Wai Hung and Wan-Chi Siu "Computationally scalable adaptive image interpolation algorithm using maximum-likelihood denoising for real-time applications," Journal of Electronic Imaging 22(4), 043006 (9 October 2013). https://doi.org/10.1117/1.JEI.22.4.043006

Published: 9 October 2013

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