JOURNAL ARTICLE

A block-based super-resolution for video sequences

Abstract

An algorithm for video resolution enhancement is presented. The approach borrows from previous methods for still-image super- resolution, introducing modifications better suited for the characteristics specific to video domain problems. Each high-resolution (HR) frame is determined through a series of MMSE spatial interpolations based on the local features (statistics) of the frame. Cross-frame registration is estimated externally and the reconstruction algorithm does not limit the form of the motion model, unlike previous data-fusion/deconvolution approaches which have required motion models that do not alter the point-spread function (i.e., motion/blur commutability). This feature is made possible using a reverse motion model mapping the locations of desired HR pixels onto their corresponding locations in the observation frames. An ticipating the existence of registration error found in typical video sequences, the algorithm also provides an internal validation of the observation pixels, helping to reduce significant mis-registration artifacts. An arbitrary enhancement factor can be used, allowing an output at any desired resolution. Interpolation and deblurring are incorporated as a single MMSE filtering operation, providing a non-iterative one-step reconstruction process. Experimental results demonstrating the capabilities of the algorithm are made available.

Keywords:
Artificial intelligence Computer vision Computer science Deblurring Interpolation (computer graphics) Deconvolution Pixel Frame (networking) Motion interpolation Motion compensation Image resolution Motion estimation Inter frame Block (permutation group theory) Algorithm Block-matching algorithm Reference frame Image restoration Motion (physics) Image (mathematics) Mathematics Video tracking Image processing Object (grammar)

Metrics

15
Cited By
0.29
FWCI (Field Weighted Citation Impact)
11
Refs
0.66
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Citation History

Topics

Advanced Image Processing Techniques
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Advanced Vision and Imaging
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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