With the advent of progressive format display and broadcast technologies, video deinterlacing has become an important video-processing technique. Numerous approaches exist in the literature to accomplish deinterlacing. While most earlier methods were simple linear filtering-based approaches, the emergence of faster computing technologies and even dedicated video-processing hardware in display units has allowed higher quality but also more computationally intense deinterlacing algorithms to become practical. Most modern approaches analyze motion and content in video to select different deinterlacing methods for various spatiotemporal regions. We introduce a family of deinterlacers that employs spectral residue to choose between and weight control grid interpolation based spatial and temporal deinterlacing methods. The proposed approaches perform better than the prior state-of-the-art based on peak signal-to-noise ratio, other visual quality metrics, and simple perception-based subjective evaluations conducted by human viewers. We further study the advantages of using soft and hard decision thresholds on the visual performance.
Interpolation is an essential and broadly employed function of signal processing. Accordingly, considerable development has focused on advancing interpolation algorithms toward optimal accuracy. Such development has motivated a clear shift in the state-of-the art from classical interpolation to more intelligent and resourceful approaches, registration-based interpolation for example. As a natural result, many of the most accurate current algorithms are highly complex, specific, and computationally demanding. However, the diverse hardware destinations for interpolation algorithms present unique constraints that often preclude use of the most accurate available options. For example, while computationally demanding interpolators may be suitable for highly equipped image processing platforms (e.g., computer workstations and clusters), only more efficient interpolators may be practical for less well equipped platforms (e.g., smartphones and tablet computers). The latter examples of consumer electronics present a design tradeoff in this regard: high accuracy interpolation benefits the consumer experience but computing capabilities are limited.
Artificial displacement (the apparent motion of stationary objects) is one important component of atmospheric
turbulence distortion, which has led many researchers to propose motion compensation as a solution. Defining a
sufficiently dense set of motion estimates for successful restoration is challenging, particularly for time sensitive
applications. We introduce a new, control grid implementation of optical
flow that allows for rapid and analytical
solutions to the motion estimation problem. Our results demonstrate the effectiveness of using the resulting
motion field for removing articial displacements in turbulence distorted videos.
KEYWORDS: Digital signal processing, Lithium, Detection and tracking algorithms, Image resolution, Control systems, Image filtering, Charge-coupled devices, CCD image sensors, Image interpolation, RGB color model
We recently reported good results with our image interpolation algorithm, One-Dimensional Control Grid Interpolation
(1DCGI), in the context of grayscale images. 1DCGI has high quantitative accuracy, flexibility with
respect to scaling factor, and low computational cost relative to similarly performing methods. Here we look to
extend our method to the demosaicing of Bayer-Patterned images. 1DCGI-based demosaicing performs quantitatively
better than the gradient-corrected linear interpolation method of Malvar. We also demonstrate effective
interpolation of full color images.